Advanced Plotting in R
Plotting in ggplot2 – Review of Core Concepts
Basic Plots
Remember Shrek?
“Layers. Onions have layers. Ogres have layers. Onions have layers. You get it? We both have layers.”
ggplot(things that impact entire plot) + geom_something(things that impact just the something)
# ggplot(things that impact entire plot) == LAYER 1!
ggplot(data = midus, aes(x = BMI, y = self_esteem))
# geom_something(things that impact just the something) == LAYER 2
# Let's make a Scatter Plot
ggplot(data = midus, aes(x = BMI, y = self_esteem)) +
geom_point()
# In order to change the names of the axes and add a title,
# you'll want to add a new LAYER!
ggplot(data = midus, aes(x = BMI, y = self_esteem)) +
geom_point() +
labs(x = "Body Mass Index (BMI)",
y = "Self-Esteem",
title = "Scatterplot of BMI by Self-Esteem")
Remove NA from within a plot
This requires truly missing values to be coded as NA and not something like 999 or . or "na".
# What happens when you don't remove the `NA` values?
ggplot(data = midus_orig, aes(x = BMI, y = self_esteem)) +
geom_point() +
labs(x = "Body Mass Index (BMI)",
y = "Self-Esteem",
title = "Scatterplot of BMI by Self-Esteem")## Warning: Removed 1172 rows containing missing values (geom_point).# Actually remove them
ggplot(data = midus, aes(x = BMI, y = self_esteem)) +
geom_point(na.rm = TRUE) +
labs(x = "Body Mass Index (BMI)",
y = "Self-Esteem",
title = "Scatterplot of BMI by Self-Esteem")
Exercise 1: Layers
The code below is broken. What is missing? HINT: run the broken code, and look at the error message!
ggplot(data = midus, aes(x = sex, y = BMI))
geom_violin() +
labs(x = "Sex", y = "Body Mass Index (BMI)",
title = "Exercise 1")Changing Aesthetics
Compare the code for the following 2 plots, and notice the differences. What happens to the shape and color of the points? What happens to the size of the plot? Why?
ggplot(data = midus, aes(x = life_satisfaction, y = self_esteem)) +
geom_point(color = "purple", shape = 3) +
labs(x = "Life Satisfaction",
y = "Self-Esteem",
title = "Changing Aesthetics",
subtitle = "Plot 1")
ggplot(data = midus, aes(x = life_satisfaction, y = self_esteem)) +
geom_point(aes(color = sex, shape = sex)) +
labs(x = "Life Satisfaction",
y = "Self-Esteem",
title = "Changing Aesthetics",
subtitle = "Plot 2")
############################################################
##### What should you do when you have overlapping points? #
##### The 'alpha' parameter controls transparency. #########
##### Notice how it's inside geom_point, but outside aes! ##
##### To make this easier to read, lets make the SIZE of ###
##### all points bigger. Notice how it's also outside aes! #
############################################################
ggplot(data = midus, aes(x = life_satisfaction, y = self_esteem)) +
geom_point(aes(color = sex, shape = sex), alpha = .6, size = 4) +
labs(x = "Life Satisfaction",
y = "Self-Esteem",
title = "Changing Aesthetics",
subtitle = "Plot 3")
## We will talk more about overlapping points below with "jittering".Bubble Plot
A bubble plot is a lot like a scatter plot, with an extra size dimension (See here for more). In ggplot2, it’s easy to make! All you have to do is put the size argument inside of aes().
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) +
geom_point(alpha = .5, aes(size = hostility)) +
labs(x = "Self-Esteem", y = "Life Satisfaction", title = "Bubble Plot")
Re-organizing Factors
# geom_bar uses count as default for the y-axis.
# This gives you the same information as the table() function
table(midus$heart_self)##
## No Yes
## 3086 654ggplot(data = midus, aes(x = heart_self)) +
geom_bar(aes(fill = heart_self))
############################################################
##### Look at the levels of your factor to see what ########
##### order things are in. Unless you tell R, it's going ###
##### to be ordered alphabetically. If you want a different#
##### order, you must re-order your factor variable, and ###
##### then plot. If you're unsure, use levels() function. ##
############################################################
# look at levels
levels(midus$heart_self)## NULL# make a new variable for the new order of levels, then
# assign that new order
new_heart_self <- c("Yes", "No")
midus$heart_self <- factor(midus$heart_self, levels = new_heart_self)
ggplot(data = midus, aes(x = heart_self)) +
geom_bar(aes(fill = heart_self))
# change things back using nested functions
midus$heart_self <- factor(midus$heart_self, levels = c("No","Yes"))
levels(midus$heart_self)## [1] "No" "Yes"Color Palettes
These are useful when you have a TON of data and want to maximize differences between colors (or you want to use colors that are colorblind friendly). Or maybe you just don’t like the default colors.
If you already know the name of the color palette you want to use, then you do not need to load any additional packages. If you want to play around, load the RColorBrewer package.
library(RColorBrewer)
# Find the Palette that you like. Look up help page for
# display.brewer.all() to learn more about the different types
display.brewer.all()
# Rather than manually set each of the colors, we can add a new LAYER
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) + # Layer 1
geom_point(aes(color = heart_self, shape = heart_self)) + # Layer 2
labs(x = "Self-Esteem", y = "Life Satisfaction", # Layer 3
title = "Color Palettes") +
scale_color_brewer(palette = "Pastel1") # Layer 4
# Let's see this when we have more than 2 levels
# Need to first create the levels -- using code from Day 3
midus$age_category <- cut(x = midus$age,
breaks = c(28, 40, 60, 84),
labels=c("young", "middle", "old"),
include.lowest = TRUE)
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) + # Layer 1
geom_point(aes(color = age_category, shape = age_category)) + # Layer 2
labs(x = "Self-Esteem", y = "Life Satisfaction", # Layer 3
title = "Color Palettes",
subtitle = "With More Factor Levels") +
scale_color_brewer(palette = "Pastel1") # Layer 4
There are TONS of color palettes. Some are great, some are kinda ridiculous. For example, there are Wes Anderson themed palettes! To view them, click here.
Themes
Themes are great when you want to change the entire look of your plot. Most of the themes you need are built into the main ggplot2 package. If you want more options, check out the ggthemes package and there are also some other individual packages (some favorites include FiveThirtyEight and XKCD themes!). Here are some of the more basic themes:
# No specified theme -- the ggplot2 default
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack?", y = "Life Satisfaction",
title = "No Set Theme")
# Black and White
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Black and White Theme") +
theme_bw()
# Black and White, but now the baseline font size is smaller
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Black and White Theme",
subtitle = "different base size") +
theme_bw(base_size = 7)
# Classic theme -- no background gridlines
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Classic Theme") +
theme_classic()
# Dark theme
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Dark Theme") +
theme_dark()
# Void Theme
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Void Theme") +
theme_void()
Exercise 2: Color Palettes & Themes
Go through the following:
- Look up the help documentation for the
display.brewer.all()function.- Use this function to find a color palette that is good for 3 colors
- Include the argument
type = "qual" - Use the function’s arguments to make this palette colorblind friendly.
- Pick whichever of these you like, and use it in the following code.
- Change the overall theme to be more minimalist.
- You might find that some of the categories overlap. Make an adjustment so you can more clearly see each distribution.
display.brewer.all(n = 3, type = "qual", colorblindFriendly = T)
# use "Paired"
ggplot(data = midus, aes(x = hostility)) +
geom_density(aes(fill = age_category), alpha = .4) +
labs(x = "Hostility",
title = "Exercise 2") +
scale_fill_brewer(palette = "Paired") +
theme_classic()
Themes Continued…
What if you like a theme, but you still want to make changes? For example, let’s say you like the black and white theme, but you still want to:
- get rid of minor grid lines
- remove the title of your legend
- center the title and subtitle
- make a black box around your legend, and fill it with the color gray
You first define your theme, then add another theme() layer that includes arguments with your specific changes. You pick an argument to change, set it equal to one of the following 4 options, and finally put your change inside of one of these 4 options. You can think of them as “wrappers”:
element_text()element_rect()element_line()element_blank()
Using this gives you an overwhelming amount of flexibility! This is where Google is your friend!
# Black and white theme without changes
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Black and White Theme") +
theme_bw()
# Black and white theme with changes
ggplot(data = midus, aes(x = heart_father, y = life_satisfaction)) +
geom_violin(aes(fill = heart_father)) +
labs(x = "Dad Heart Attack", y = "Life Satisfaction",
title = "Black and White Theme") +
theme_bw() +
theme(panel.grid.major = element_line(color = NA),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
legend.background = element_rect(color = "black",
fill = "gray"))
Manually Changing Things
There are many ways to change something within ggplot2. Below is just a selection of those frequently used. To find exact values, use Google!
# Shapes
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Self-Esteem", y = "Life Satisfaction",
title = "Manually Setting Shapes") +
scale_shape_manual(values = c(9,10,11))
# Make all colors various shades of GREY for black/white printing
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) +
geom_boxplot(aes(fill = age_category)) +
labs(x = "Self-Esteem", y = "Life Satisfaction",
title = "All Grey") +
scale_fill_grey(start = 0, end = 0.8,
labels = c("young", "middle", "old"))
# Changing the legend: location, title, and labels
# ONlY change the title
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category), alpha = .5) +
labs(x = "Self-Esteem", y = "Life Satisfaction",
title = "Legend Change",
color = "Age (by group)",
shape = "Age (by group)")
# Change the title, location, and labels at once
ggplot(data = midus, aes(x = self_esteem, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category), alpha = .5)+
labs(x = "Self-Esteem", y = "Life Satisfaction",
title = "Legend Change Part 2") +
scale_color_discrete(name = "Age (by group)",
labels = c("YOUNG", "MID", "OLD")) +
scale_shape(guide = FALSE) + #removes the legend for shapes
theme(legend.position = "bottom")
# Change the scale of the x-axis
## Original plot
ggplot(data = midus, aes(x = self_esteem)) +
geom_density(aes(fill = heart_self), alpha = .5) +
labs(x = "Self-Esteem", y = "Density", title = "Coord Change",
subtitle = "Default") +
theme_classic()
## With modified x-axis
ggplot(data = midus, aes(x = self_esteem)) +
geom_density(aes(fill = heart_self), alpha = .5) +
labs(x = "Self-Esteem", y = "Density", title = "Coord Change",
subtitle = "Default") +
theme_classic() +
xlim(c(0,75))
# Change the angle of text on an axis - ONLY ANGLE CHANGE
ggplot(data = midus, aes(x = age_category)) +
geom_bar(aes(fill = age_category)) +
labs(subtitle = "only changing the angle") +
theme_light() +
theme(axis.text.x = element_text(angle = 90))
# Change the angle of text on an axis - ANGLE & VERTICAL ALIGNMENT
ggplot(data = midus, aes(x = age_category)) +
geom_bar(aes(fill = age_category)) +
labs(subtitle = "changing the angle and vertical alignment") +
theme_light() +
theme(axis.text.x = element_text(angle = 90, vjust = .5))
# Change the angle of text on an axis - ANGLE & VERTICAL ALIGNMENT
# & HORIZONTAL ALIGNMENT
ggplot(data = midus, aes(x = age_category)) +
geom_bar(aes(fill = age_category)) +
labs(subtitle ="angle, vertical,\nand horizontal aligntment") +
theme_light() +
theme(axis.text.x = element_text(angle = 90, vjust = .5,
hjust = 1))
# What about different angles?
ggplot(data = midus, aes(x = age_category)) +
geom_bar(aes(fill = age_category)) +
labs(subtitle ="45 degree angle") +
theme_light() +
theme(axis.text.x = element_text(angle = 45, vjust = 1,
hjust = 1))
Exercise 3: Ugliest Plot in the World
The code below makes THE ugliest plot ever. Your job is to work backwards and turn this into a plot that is publication-ready (and not super ugly). Do so without deleting any whole layers. Just modify the existing code.
ggplot(data = midus, aes(x = life_satisfaction, y = self_esteem)) +
geom_point(aes(shape = age_category, color = age_category),
size = 3, alpha = .5) +
scale_shape_manual(values = c(16,17,18)) +
scale_color_brewer(palette = "Dark2") +
theme_classic() +
theme(axis.text.x = element_text(color = "black",
face = "bold",
angle = 0),
axis.text.y = element_text(color = "black",
#family = "Times",
face = "bold",
angle = 0),
axis.title = element_text(face = "italic",
#family = "Times",
size = 10),
plot.background = element_rect(fill = "white"),
legend.position = "top")
Faceting
Faceting lets you break up your plot into multiple sub-plots. There are two types: facet_grid and facet_wrap. Within each, read the ~ as “by”.
facet_grid
Especially great when you have multiple factors to separate on. If you want to change the x- and y-axes so that they best fit the data (rather than keep them the exact same across subplots), use the scales argument (see Plot 2 below).
Like the name implies, facet_grid is going to make a grid. Just like a matrix, the right hand side is rows and the left hand side is columns (i.e., 2x3 matrix = 2 rows and 3 columns). You can put your factor on either side, but that will change the layout of your grid! For example:
~ age_categoryis read as “by age category”, and is in the column position. The result is a 1x3 grid. There are 3 columns becauseage_categoryhas 3 levels.age_category ~is read as “age category by”. This doesn’t work! You need something else. To indicate that you do not want to facet by any additional factor, use the.. So the correct syntax for facetingage_categoryin the row position isage_category ~ .. The result will be a 3x1 grid.
# Plot 1 = 1 variable to separate on, keep the axes the same
# for each sub-plot
ggplot(data = midus, aes(x = BMI, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Body Mass Index (BMI)", y = "Life Satisfaction",
title = "Facet_Grid: Plot 1a",
subtitle = "facet along columns") +
facet_grid(~ age_category)
# Plot 2 = 1 variable to separate on, let the x-axis change based
# on the actual data
ggplot(data = midus, aes(x = BMI, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Body Mass Index (BMI)", y = "Life Satisfaction",
title = "Facet_Grid: Plot 1b",
subtitle = "facet along columns, x-axes based on data") +
facet_grid(~ age_category, scales = "free_x")
ggplot(data = midus, aes(x = BMI, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Body Mass Index (BMI)", y = "Life Satisfaction",
title = "Facet_Grid: Plot 1c",
subtitle = "facet along rows") +
facet_grid(age_category ~.)
Now let’s see what happens when you facet based on 2 different factor variables. To do this, lets create a new categorical variable.
############################################################
##### But first, what happens when a categorical variable ##
##### is treated continuously, and not as a factor? ########
############################################################
# This creates a new variable called "session", and is scored as 1
# and 2. However, it will be treated as a numeric variable
session <- c(rep("1", times = 935), rep("2", times = 935))
midus["session"] <- rep(session, times = 2)
midus$session <- as.numeric(midus$session)
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = session))
# If you are working with a categorical variable, it is
# strongly recommended that you tell R to treat it as a factor.
# It is easier to keep categorical variables as words, rather than
# numeric codes. That is, rather than 1 and 2, let's instead
# use "session1" and "session2" for the new "session" variable. Let's recode
# this variable such that 1 = "session1" and 2 = "session2". Make sure
# R treats this as a "factor", rather than "character". You can
# always check using the "class()" function.
midus$session <- factor(midus$session, labels = c("session1", "session2"))
class(midus$session)## [1] "factor"ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = session))
######################################
# Ok, back to faceting
# Plot 3 = 2 variables to separate on, age_category x wave
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Hostility", y = "Life Satisfaction",
title = "Facet_Grid: Plot 3") +
facet_grid(session ~ age_category)
# Plot 4 = 2 variables to separate on, species x time
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Hostility", y = "Life Satisfaction",
title = "Facet_Grid: Plot 4") +
facet_grid(age_category ~ session)
facet_wrap
This basically creates a ribbon that will just continue on to the next row when ready. This is useful for when you have categorical variables, but they don’t necessarily need to be in a grid or matrix format. For instance, if one of the facet_grid cells would be empty, you probably don’t want to show an empty plot (a plot with no points/shapes – you’d rather it be just blank space). This does not follow the rows by columns syntax – it will always just go to the next row.
# Plot 1 = 1 variable to separate on, use default rows/columns
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Life Satisfaction", y = "Hostility",
title = "Facet_Wrap: Plot 1") +
facet_wrap(~ age_category)
# Plot 2 = 1 variable to separate on, change number of
# columns of plots
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Life Satisfaction", y = "Hostility",
title = "Facet_Wrap: Plot 1") +
facet_wrap(~ age_category, ncol = 1)
# Plot 3 = 2 variables to separate on
ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category, shape = age_category)) +
labs(x = "Life Satisfaction", y = "Hostility",
title = "Facet_Wrap: Plot 1") +
facet_wrap(~ age_category + session, ncol = 4)
Exercise 4: Faceting
Exercise 4a: Imagine each of the 3740 midus observations were randomly assigned to belong to one of two groups. Add to the code below to facet based on session (from above) and this new group variable. The result should include 4 subplots. Organize them such that they are in a 2x2 layout, with group 1 in the left column, group 2 in the right column, session1 in the first row, and session2 in the second row.
# midus$group <- sample(x = c("group1", "group2"), size = 3740,
# replace = TRUE)
# midus$group <- factor(midus$group)
#
# ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
# geom_point(aes(color = age_category)) +Exercise 4b: Now, use facet_wrap to get subplots reflecting all 3 factor variables (age_category, group, and session). How many subplots should you wind up with? Organize them such that there are 3 columns.
# ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
# geom_point(aes(color = age_category)) +Arranging Multiple Plots
Let’s say you have 3 different, independent plots (not subplots) that you want to arrange in to a cohesive figure. You’re arranging a grid of plots. There are 2 ways of doing this. The newer way is with a package called ggpubr and the function is ggarrange(). The older way is with the gridExtra package. Both will work, but for now, let’s use the newer method.
Importantly, you need to STORE these plots to your environment first (which means it won’t immediately appear when you run the code). Then you can arrange the plots based on the name you assigned it.
library(ggpubr)
# First, create your plots! If you want to have a single legend for every plot (ex: the colors are the same for all plots), then make sure your plots are coded accordingly. Then, ggarrange
# plot A - scatter plot
plotA <- ggplot(data = midus, aes(x = hostility, y = life_satisfaction)) +
geom_point(aes(color = age_category)) +
theme_minimal() +
labs(title = "Scatter Plot")
# plot B - histogram
plotB <- ggplot(data = midus, aes(x = life_satisfaction)) +
geom_histogram(binwidth = .1, aes(fill = age_category), alpha = .7) +
theme_minimal() +
labs(title = "Histogram")
# plot C - violin plot
plotC <- ggplot(data = midus, aes(x = age_category, y = life_satisfaction)) +
geom_violin(aes(fill = age_category)) +
theme_minimal() +
labs(title = "Violin Plot")
ggarrange(plotA, plotB, plotC, nrow = 2, ncol = 2,
common.legend = TRUE,
labels = c("A", "B", "C"), legend = "bottom")
############################################################
##### This is fine, but it's kind of smushed. What you'd ###
##### actually want to see is the bottom violin plot taking#
##### up the entire 2 columns! This would be even better if#
##### the violin plot had the distributions stacked ########
##### vertically, rather than horizontally. Let's do it! ###
############################################################
# To change the violin plot, all we need to do is flip the coordinates
plotCNew <- ggplot(data = midus, aes(x = age_category, y = life_satisfaction)) +
geom_violin(aes(fill = age_category)) +
theme_minimal() +
labs(title = "Violin Plot") +
coord_flip()
# Let's look!
plotC
plotCNew
# OK, looks great. Now what we're going to do is nest 2 ggarrange
# objects together. The first (inner most) object will combine
# plots A and B into 1 plot. Then, we will add our new plotCNew.
# The idea is that there are 2 columns for A & B, but only 1
# column for C. This means C has room to take up the full width.
ggarrange(ggarrange(plotA, plotB, ncol = 2, labels = c("A", "B"),
legend = "none"),
plotCNew, nrow = 2, common.legend = TRUE,
legend = "bottom", labels = c("", "C"))
# Final note about ggarrange: It does not like when ncol or nrow = 1. If you
# want a plot with 1 column and 3 rows, do *NOT* specify ncol = 1. Instead
# use nrow = 3. Adding Lines & Text
Horizontal & Vertical Lines
ggplot(data = midus, aes(x = BMI, y = self_esteem)) +
geom_point(aes(color = age_category)) +
geom_vline(xintercept = mean(midus$BMI), color = "red") +
labs(x = "Body Mass Index (BMI)", y = "Self-Esteem",
title = "Vertical Mean Lines for BMI")
############################################################
##### What if you want it by each group? Need to create ####
##### that data, store it in an object, and then call it ###
##### from within the plot. Look at all these LAYERS! ######
############################################################
meansLength <- midus %>%
group_by(age_category) %>%
summarize(xint = mean(life_satisfaction))
ggplot(data = midus, aes(x = life_satisfaction, y = self_esteem)) +
geom_point(aes(color = age_category), alpha = .3) +
geom_vline(data = meansLength, aes(xintercept = meansLength$xint,
color = meansLength$age_category,
linetype = meansLength$age_category),
size = 1.5) +
labs(x = "Life Satisfaction", y = "Self Esteem",
title = "Vertical Mean Lines for Life Satisfaction, by Age") +
theme_classic() +
scale_color_brewer(palette = "Dark2")
Exercise 5: Horizontal Lines
We just added vertical lines to represent the mean of life_satisfaction per age_category. Change the code below to represent the mean of self_esteem per age_category. Plot these means as horizontal lines. HINT: You do not need to change the meansWidth code, only the plotting code. BONUS: How can you get rid of the legend that shows the linetype?
# meansWidth <- midus %>%
# group_by(age_category) %>%
# summarize(yint = mean(self_esteem))
#
# ggplot(data = midus, aes(x = BMI, y = self_esteem)) +
# geom_point(aes(color = age_category)) +
# labs(x = "BMI", y = "Self Esteem",
# title = "Horizontal Mean Lines for Self-Esteem, by Age") For simple linear regression, you only need to add geom_smooth
ggplot(data = midus, aes(x = hostility, y = self_esteem)) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic() +
labs(title = "Regression Plot 1a", subtitle = "Simple Linear",
x = "Hostility", y = "Self Esteem")
ggplot(data = midus, aes(x = hostility, y = self_esteem)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
theme_classic() +
labs(title = "Regression Plot 1b", subtitle = "Simple Linear",
x = "Hostility", y = "Self Esteem")
# Note: If you want a line that is not vertical/horizontal,
# use geom_abline. You'll need an intercept and slope. Usually,
# we're talking about regression lines, and geom_smooth is easier.
For plotting complex interactions from mulitple regressions, check out the ggpredict() function from the ggeffects package!
If you want to annotate a plot with text, you can add an annotate layer.
# Nerdy Stats Warning!
# Simple linear regression coefficient of 2 standardized variables = correlation
corSelfEsteemLifeSat <- round(cor(scale(midus$self_esteem),
scale(midus$life_satisfaction)),
digits = 2)
ggplot(data = midus, aes(x = scale(midus$self_esteem),
y = scale(midus$life_satisfaction))) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
theme_classic() +
annotate(geom = "text", x = 1, y = -5,
label = paste0("r = ", corSelfEsteemLifeSat)) +
xlim(c(-4,3)) +
ylim(c(-6,3)) +
labs(title = "Annotating Plots",
x = "Self Esteem",
y = "Life Satisfaction")
# BONUS CHALLENGE: How could you check that the regression coefficient of the
# standardized variables is the same as the correlation of the original variables?Layers on Layers on Layers
Bar Plots and Error Bars
Thus far, we have not seen many bar plots (besides simple counts) or error bars. Usually, bar plots reflect the mean of a group. Just like we did with the vertical/horizontal lines, we’ll want to transform our raw data into summary statistics. If you decide to work with summary stats, it is suggested that you create your summarized dataset outside of ggplot first, then call that from inside the ggplot function. It also means you can easily make your error bars.
# first, let's summarize the raw data
# and create a new data.frame with summary statistics
# This type of code comes from "tidyverse". As you get more familiar with R,
# we recommend checking this out!
summaryStats <- midus %>%
group_by(age_category, sex) %>%
summarize(means = mean(physical_health_self),
sds = sd(physical_health_self)) %>%
mutate(sdLower = means - sds) %>%
mutate(sdUpper = means + sds)
# now, let's plot!
ggplot(data = summaryStats, aes(x = age_category,
y = means,
fill = sex)) +
geom_col(position = position_dodge(width=.9)) +
geom_errorbar(aes(ymin = sdLower, ymax = sdUpper),
position = position_dodge(width=.9),
width = .2) +
theme_classic() +
scale_fill_brewer(palette = "Accent",
labels = c("mulheres", "homens")) +
labs(title = "Error Bars",
x = "",
y = "Mean of Physical Health\n(self-reported)",
fill = "Gender") 
Exercise 6: Bar Plots and Error Bars
Make a bar plot where the x-axis is sex, and contains bars for each of the 3 age_category categories. The legend should reflect that each age_category is a different color. Add error bars reflecting standard deviation.
# ggplot(data = summaryStats, aes(x = , y = ,
# fill = )) +
# geom_col(position = position_dodge(width = .9)) +Plotting Multiple Geoms
By now you’ve hopefully realized that you can add as many layers as you’d like to your ggplot. This means you can use multiple shapes or geom_s from the same data on the same plot. Let’s look at line graphs and violin plots, as examples
############################################################
##### Like the bar graph above, let's say you want to know #
##### how the mean of physical health changes per category #
##### and per sex. A line connecting points might helpful! #
############################################################
ggplot(data = summaryStats, aes(x = age_category, y = means,
group = sex)) +
geom_point(aes(color = sex, shape = sex), size = 3) +
geom_errorbar(aes(ymin = sdLower, ymax = sdUpper,
color = sex), width = .1,
linetype = "dashed") +
ylim(c(0,6)) +
geom_line(aes(color = sex)) +
labs(y = "Mean Physical Health",
x = "",
color = "",
title = "Line Graph") +
scale_shape_discrete(guide = FALSE)
Exercise 7: Interpretation
What would you conclude from the above plot? What statistical test could you run to formally test this? In the empty chunk below, run this test!
What if you wanted to look at individual points in your dataset that are overlayed onto something else? Like overlaying the distribution of scores on top of a violin plot.
############################################################
##### A lot of these points overlap. Even if we change the #
##### transparency, it's still going to be difficult to ####
##### view all the points. To fix, this let's add a jitter.#
##### Jitter = slight irregularity, variance, unsteadiness.#
##### Think uneasy or nervous -- kinda shaky. We can jitter#
##### the points on the x-axis to randomly shift them ######
##### slightly. You can set how big you want the jitter to #
##### be using `width`. ####################################
############################################################
ggplot(data = midus, aes(x = hostility,
y = self_esteem)) +
geom_point(aes(color = age_category)) +
theme_classic() +
scale_color_brewer(palette = "Set2") +
labs(x = "Hostility",
y = "Self Esteem",
title = "What Is This Graph?",
subtitle = "seriously, it's so bad")
ggplot(data = midus, aes(x = hostility,
y = self_esteem)) +
geom_jitter(aes(color = age_category), width = 1) +
theme_classic() +
scale_color_brewer(palette = "Set2") +
labs(x = "Hostility",
y = "Self Esteem",
title = "Jitter")
# Now, violin plots show distributions. But using the code from above
# we can superimpose the individual points onto the violin plot.
# In order to make this readable, let's change the transparency of
# all the violins to be very light. LAYERS!
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
geom_violin(aes(fill = Species), alpha = .3) +
geom_point(aes(color = Species),
position = position_jitter(width = .1)) +
labs(x = "", y = "Length (cm)",
title = "Distributions of Sepal Length",
subtitle = "Per Iris Species") +
theme_classic()
# Wanna add a boxlplot for good measure? Add another layer!
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
geom_violin(aes(fill = Species), alpha = .3) +
geom_point(aes(color = Species),
position = position_jitter(width = .1)) +
geom_boxplot(aes(fill = Species), alpha = .3) +
labs(x = "", y = "Length (cm)",
title = "Distributions of Sepal Length",
subtitle = "Per Iris Species") +
theme_classic()
# Violin + Boxplot Only
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
geom_violin(aes(fill = Species), alpha = .3) +
geom_boxplot(aes(color = Species), fill = "white", width = .1) +
labs(x = "", y = "Length (cm)",
title = "Distributions of Sepal Length",
subtitle = "Per Iris Species") +
theme_classic()
# Violin + Points in a different combination
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
geom_violin(aes(color = Species), fill = "white") +
geom_point(aes(color = Species),
position = position_jitter(width = .1)) +
labs(x = "", y = "Length (cm)",
title = "Distributions of Sepal Length",
subtitle = "Per Iris Species") +
theme_classic()
Last Remaining Thoughts
Adding on to a plot
Thus far, every plot has been defined in its entirety. This is to reinforce everything that we’ve learned! However, you may run across help pages that store plots as objects, and then add to them. You should do what you find to be the most readable. I (Shelly) personally find this method to be hard to follow once the plotting gets complex. You may like it more.
p <- ggplot(data = iris)
p <- p + geom_point(aes(x = Sepal.Length, y = Sepal.Width, color = Species))
p
Exporting your plots
What’s the point of making all these publication-ready plots if you can’t get them out of R? You can use the function ggsave(). This should work most of the time, however if the journal requires something more specific, try the second method below (it gives finer control).
Important: You must consider your working directory! Your computer is basically a series of folders. Folders are also known as “directories”. Your “working directory” is where R is going to both look for files (like when reading in datasets) and where it will save (this is true of any file type: plots, .csv, .txt, etc.).
############################################################
##### It's 2019! SAVE YOUR FILES TO BOX/DROPBOX/GITHUB #####
############################################################
# Method 1: ggsave
ggsave(filename = "plotSaveTest.png",
width = 4, height = 4, units = "in")
# Method 2: Graphic Device. You first call the device, then plot, then
# turn device off.
tiff(filename = "plotSave2.tiff", width = 7, height = 7, units = "in",
res = 300, compression = "lzw") # Turn on device
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) + # Now plot
geom_point(aes(color = Species)) +
labs(x = "", y = "Length")
dev.off() # Turn off device## quartz_off_screen
## 2When things go wrong
If your code isn’t working, and you really think that it should, you might be using a function that comes from a different package. For example, the psych package also has a function called alpha. If this gives you trouble, just specify the package. EX: ggplot2::alpha.
If you want to cry because your plot still isn’t giving you what you want, try storing your plot as an object, and then look at its inner workings. Let’s look at a plot with multiple layers for example.
plotObject <- ggplot(data = iris,
aes(x = Species, y = Sepal.Length)) +
geom_violin(aes(fill = Species), alpha = .3) +
geom_point(aes(color = Species),
position = position_jitter(width = .1)) +
labs(x = "", y = "Length (cm)",
title = "Distributions of Sepal Length",
subtitle = "Per Iris Species") +
theme_classic()
ggplot_build(plotObject)## $data
## $data[[1]]
## fill x density scaled ndensity count n y PANEL
## 1 #F8766D 1 0.23622483 0.19051206 0.19051206 11.811242 50 4.300000 1
## 2 #F8766D 1 0.24043963 0.19391124 0.19391124 12.021981 50 4.302935 1
## 3 #F8766D 1 0.24464567 0.19730335 0.19730335 12.232284 50 4.305871 1
## 4 #F8766D 1 0.24884538 0.20069036 0.20069036 12.442269 50 4.308806 1
## 5 #F8766D 1 0.25302586 0.20406186 0.20406186 12.651293 50 4.311742 1
## 6 #F8766D 1 0.25719771 0.20742640 0.20742640 12.859885 50 4.314677 1
## 7 #F8766D 1 0.26135035 0.21077545 0.21077545 13.067518 50 4.317613 1
## 8 #F8766D 1 0.26548367 0.21410891 0.21410891 13.274183 50 4.320548 1
## 9 #F8766D 1 0.26960483 0.21743257 0.21743257 13.480241 50 4.323483 1
## 10 #F8766D 1 0.27369108 0.22072808 0.22072808 13.684554 50 4.326419 1
## 11 #F8766D 1 0.27776339 0.22401233 0.22401233 13.888169 50 4.329354 1
## 12 #F8766D 1 0.28180714 0.22727356 0.22727356 14.090357 50 4.332290 1
## 13 #F8766D 1 0.28582156 0.23051114 0.23051114 14.291078 50 4.335225 1
## 14 #F8766D 1 0.28982007 0.23373588 0.23373588 14.491003 50 4.338160 1
## 15 #F8766D 1 0.29376983 0.23692131 0.23692131 14.688491 50 4.341096 1
## 16 #F8766D 1 0.29770150 0.24009215 0.24009215 14.885075 50 4.344031 1
## 17 #F8766D 1 0.30159838 0.24323493 0.24323493 15.079919 50 4.346967 1
## 18 #F8766D 1 0.30545735 0.24634713 0.24634713 15.272867 50 4.349902 1
## 19 #F8766D 1 0.30929709 0.24944383 0.24944383 15.464855 50 4.352838 1
## 20 #F8766D 1 0.31308031 0.25249495 0.25249495 15.654016 50 4.355773 1
## 21 #F8766D 1 0.31684111 0.25552798 0.25552798 15.842056 50 4.358708 1
## 22 #F8766D 1 0.32056410 0.25853052 0.25853052 16.028205 50 4.361644 1
## 23 #F8766D 1 0.32424272 0.26149727 0.26149727 16.212136 50 4.364579 1
## 24 #F8766D 1 0.32789993 0.26444677 0.26444677 16.394997 50 4.367515 1
## 25 #F8766D 1 0.33149798 0.26734854 0.26734854 16.574899 50 4.370450 1
## 26 #F8766D 1 0.33507011 0.27022942 0.27022942 16.753506 50 4.373386 1
## 27 #F8766D 1 0.33860439 0.27307976 0.27307976 16.930219 50 4.376321 1
## 28 #F8766D 1 0.34209059 0.27589133 0.27589133 17.104529 50 4.379256 1
## 29 #F8766D 1 0.34555473 0.27868512 0.27868512 17.277737 50 4.382192 1
## 30 #F8766D 1 0.34896120 0.28143239 0.28143239 17.448060 50 4.385127 1
## 31 #F8766D 1 0.35233983 0.28415721 0.28415721 17.616992 50 4.388063 1
## 32 #F8766D 1 0.35568315 0.28685355 0.28685355 17.784157 50 4.390998 1
## 33 #F8766D 1 0.35897771 0.28951056 0.28951056 17.948885 50 4.393933 1
## 34 #F8766D 1 0.36225106 0.29215047 0.29215047 18.112553 50 4.396869 1
## 35 #F8766D 1 0.36547170 0.29474788 0.29474788 18.273585 50 4.399804 1
## 36 #F8766D 1 0.36866445 0.29732279 0.29732279 18.433222 50 4.402740 1
## 37 #F8766D 1 0.37182636 0.29987282 0.29987282 18.591318 50 4.405675 1
## 38 #F8766D 1 0.37494192 0.30238548 0.30238548 18.747096 50 4.408611 1
## 39 #F8766D 1 0.37803842 0.30488276 0.30488276 18.901921 50 4.411546 1
## 40 #F8766D 1 0.38108979 0.30734365 0.30734365 19.054489 50 4.414481 1
## 41 #F8766D 1 0.38411509 0.30978352 0.30978352 19.205755 50 4.417417 1
## 42 #F8766D 1 0.38711527 0.31220312 0.31220312 19.355763 50 4.420352 1
## 43 #F8766D 1 0.39007434 0.31458957 0.31458957 19.503717 50 4.423288 1
## 44 #F8766D 1 0.39301748 0.31696317 0.31696317 19.650874 50 4.426223 1
## 45 #F8766D 1 0.39592467 0.31930778 0.31930778 19.796233 50 4.429159 1
## 46 #F8766D 1 0.39880936 0.32163424 0.32163424 19.940468 50 4.432094 1
## 47 #F8766D 1 0.40167514 0.32394545 0.32394545 20.083757 50 4.435029 1
## 48 #F8766D 1 0.40450733 0.32622958 0.32622958 20.225367 50 4.437965 1
## 49 #F8766D 1 0.40732734 0.32850387 0.32850387 20.366367 50 4.440900 1
## 50 #F8766D 1 0.41012117 0.33075706 0.33075706 20.506058 50 4.443836 1
## 51 #F8766D 1 0.41289743 0.33299607 0.33299607 20.644871 50 4.446771 1
## 52 #F8766D 1 0.41566085 0.33522474 0.33522474 20.783043 50 4.449706 1
## 53 #F8766D 1 0.41839970 0.33743358 0.33743358 20.919985 50 4.452642 1
## 54 #F8766D 1 0.42113037 0.33963583 0.33963583 21.056519 50 4.455577 1
## 55 #F8766D 1 0.42384427 0.34182455 0.34182455 21.192213 50 4.458513 1
## 56 #F8766D 1 0.42654641 0.34400379 0.34400379 21.327321 50 4.461448 1
## 57 #F8766D 1 0.42924111 0.34617703 0.34617703 21.462055 50 4.464384 1
## 58 #F8766D 1 0.43192090 0.34833824 0.34833824 21.596045 50 4.467319 1
## 59 #F8766D 1 0.43459638 0.35049597 0.35049597 21.729819 50 4.470254 1
## 60 #F8766D 1 0.43726342 0.35264691 0.35264691 21.863171 50 4.473190 1
## 61 #F8766D 1 0.43992483 0.35479330 0.35479330 21.996241 50 4.476125 1
## 62 #F8766D 1 0.44258316 0.35693721 0.35693721 22.129158 50 4.479061 1
## 63 #F8766D 1 0.44523604 0.35907672 0.35907672 22.261802 50 4.481996 1
## 64 #F8766D 1 0.44788805 0.36121553 0.36121553 22.394403 50 4.484932 1
## 65 #F8766D 1 0.45053842 0.36335301 0.36335301 22.526921 50 4.487867 1
## 66 #F8766D 1 0.45318904 0.36549070 0.36549070 22.659452 50 4.490802 1
## 67 #F8766D 1 0.45583985 0.36762855 0.36762855 22.791993 50 4.493738 1
## 68 #F8766D 1 0.45849360 0.36976876 0.36976876 22.924680 50 4.496673 1
## 69 #F8766D 1 0.46114936 0.37191059 0.37191059 23.057468 50 4.499609 1
## 70 #F8766D 1 0.46380854 0.37405518 0.37405518 23.190427 50 4.502544 1
## 71 #F8766D 1 0.46647318 0.37620418 0.37620418 23.323659 50 4.505479 1
## 72 #F8766D 1 0.46914058 0.37835540 0.37835540 23.457029 50 4.508415 1
## 73 #F8766D 1 0.47181705 0.38051394 0.38051394 23.590853 50 4.511350 1
## 74 #F8766D 1 0.47449815 0.38267620 0.38267620 23.724907 50 4.514286 1
## 75 #F8766D 1 0.47718605 0.38484396 0.38484396 23.859302 50 4.517221 1
## 76 #F8766D 1 0.47988371 0.38701959 0.38701959 23.994186 50 4.520157 1
## 77 #F8766D 1 0.48258600 0.38919894 0.38919894 24.129300 50 4.523092 1
## 78 #F8766D 1 0.48530136 0.39138884 0.39138884 24.265068 50 4.526027 1
## 79 #F8766D 1 0.48802360 0.39358429 0.39358429 24.401180 50 4.528963 1
## 80 #F8766D 1 0.49075463 0.39578683 0.39578683 24.537731 50 4.531898 1
## 81 #F8766D 1 0.49349877 0.39799994 0.39799994 24.674939 50 4.534834 1
## 82 #F8766D 1 0.49624881 0.40021781 0.40021781 24.812441 50 4.537769 1
## 83 #F8766D 1 0.49901427 0.40244812 0.40244812 24.950714 50 4.540705 1
## 84 #F8766D 1 0.50178849 0.40468549 0.40468549 25.089425 50 4.543640 1
## 85 #F8766D 1 0.50457247 0.40693073 0.40693073 25.228624 50 4.546575 1
## 86 #F8766D 1 0.50737220 0.40918867 0.40918867 25.368610 50 4.549511 1
## 87 #F8766D 1 0.51017868 0.41145206 0.41145206 25.508934 50 4.552446 1
## 88 #F8766D 1 0.51300186 0.41372891 0.41372891 25.650093 50 4.555382 1
## 89 #F8766D 1 0.51583547 0.41601418 0.41601418 25.791774 50 4.558317 1
## 90 #F8766D 1 0.51867923 0.41830763 0.41830763 25.933961 50 4.561252 1
## 91 #F8766D 1 0.52154104 0.42061564 0.42061564 26.077052 50 4.564188 1
## 92 #F8766D 1 0.52441028 0.42292965 0.42292965 26.220514 50 4.567123 1
## 93 #F8766D 1 0.52729705 0.42525779 0.42525779 26.364853 50 4.570059 1
## 94 #F8766D 1 0.53019600 0.42759575 0.42759575 26.509800 50 4.572994 1
## 95 #F8766D 1 0.53310527 0.42994204 0.42994204 26.655263 50 4.575930 1
## 96 #F8766D 1 0.53603510 0.43230490 0.43230490 26.801755 50 4.578865 1
## 97 #F8766D 1 0.53897311 0.43467437 0.43467437 26.948656 50 4.581800 1
## 98 #F8766D 1 0.54192958 0.43705872 0.43705872 27.096479 50 4.584736 1
## 99 #F8766D 1 0.54490038 0.43945463 0.43945463 27.245019 50 4.587671 1
## 100 #F8766D 1 0.54788173 0.44185905 0.44185905 27.394086 50 4.590607 1
## 101 #F8766D 1 0.55088691 0.44428268 0.44428268 27.544345 50 4.593542 1
## 102 #F8766D 1 0.55390130 0.44671375 0.44671375 27.695065 50 4.596477 1
## 103 #F8766D 1 0.55693553 0.44916081 0.44916081 27.846777 50 4.599413 1
## 104 #F8766D 1 0.55998700 0.45162178 0.45162178 27.999350 50 4.602348 1
## 105 #F8766D 1 0.56304948 0.45409163 0.45409163 28.152474 50 4.605284 1
## 106 #F8766D 1 0.56614026 0.45658430 0.45658430 28.307013 50 4.608219 1
## 107 #F8766D 1 0.56924172 0.45908559 0.45908559 28.462086 50 4.611155 1
## 108 #F8766D 1 0.57236503 0.46160449 0.46160449 28.618251 50 4.614090 1
## 109 #F8766D 1 0.57550951 0.46414047 0.46414047 28.775475 50 4.617025 1
## 110 #F8766D 1 0.57866568 0.46668588 0.46668588 28.933284 50 4.619961 1
## 111 #F8766D 1 0.58185617 0.46925896 0.46925896 29.092808 50 4.622896 1
## 112 #F8766D 1 0.58505928 0.47184223 0.47184223 29.252964 50 4.625832 1
## 113 #F8766D 1 0.58828682 0.47444520 0.47444520 29.414341 50 4.628767 1
## 114 #F8766D 1 0.59154068 0.47706939 0.47706939 29.577034 50 4.631703 1
## 115 #F8766D 1 0.59480795 0.47970440 0.47970440 29.740398 50 4.634638 1
## 116 #F8766D 1 0.59811529 0.48237172 0.48237172 29.905764 50 4.637573 1
## 117 #F8766D 1 0.60143858 0.48505191 0.48505191 30.071929 50 4.640509 1
## 118 #F8766D 1 0.60478927 0.48775419 0.48775419 30.239463 50 4.643444 1
## 119 #F8766D 1 0.60817261 0.49048281 0.49048281 30.408630 50 4.646380 1
## 120 #F8766D 1 0.61157188 0.49322427 0.49322427 30.578594 50 4.649315 1
## 121 #F8766D 1 0.61501670 0.49600247 0.49600247 30.750835 50 4.652250 1
## 122 #F8766D 1 0.61848200 0.49879719 0.49879719 30.924100 50 4.655186 1
## 123 #F8766D 1 0.62197774 0.50161646 0.50161646 31.098887 50 4.658121 1
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## 558 #00BA38 2 0.21879544 0.29889530 0.29889530 10.939772 50 5.084932 1
## 559 #00BA38 2 0.22030089 0.30095188 0.30095188 11.015045 50 5.089041 1
## 560 #00BA38 2 0.22182252 0.30303057 0.30303057 11.091126 50 5.093151 1
## 561 #00BA38 2 0.22336203 0.30513369 0.30513369 11.168101 50 5.097260 1
## 562 #00BA38 2 0.22492795 0.30727289 0.30727289 11.246398 50 5.101370 1
## 563 #00BA38 2 0.22650715 0.30943022 0.30943022 11.325357 50 5.105479 1
## 564 #00BA38 2 0.22812522 0.31164066 0.31164066 11.406261 50 5.109589 1
## 565 #00BA38 2 0.22975222 0.31386329 0.31386329 11.487611 50 5.113699 1
## 566 #00BA38 2 0.23142800 0.31615257 0.31615257 11.571400 50 5.117808 1
## 567 #00BA38 2 0.23311732 0.31846034 0.31846034 11.655866 50 5.121918 1
## 568 #00BA38 2 0.23485009 0.32082747 0.32082747 11.742504 50 5.126027 1
## 569 #00BA38 2 0.23660976 0.32323134 0.32323134 11.830488 50 5.130137 1
## 570 #00BA38 2 0.23840533 0.32568426 0.32568426 11.920266 50 5.134247 1
## 571 #00BA38 2 0.24024334 0.32819516 0.32819516 12.012167 50 5.138356 1
## 572 #00BA38 2 0.24210748 0.33074175 0.33074175 12.105374 50 5.142466 1
## 573 #00BA38 2 0.24403175 0.33337049 0.33337049 12.201588 50 5.146575 1
## 574 #00BA38 2 0.24597019 0.33601858 0.33601858 12.298510 50 5.150685 1
## 575 #00BA38 2 0.24798846 0.33877572 0.33877572 12.399423 50 5.154795 1
## 576 #00BA38 2 0.25002303 0.34155514 0.34155514 12.501151 50 5.158904 1
## 577 #00BA38 2 0.25212660 0.34442881 0.34442881 12.606330 50 5.163014 1
## 578 #00BA38 2 0.25426475 0.34734973 0.34734973 12.713237 50 5.167123 1
## 579 #00BA38 2 0.25645889 0.35034714 0.35034714 12.822945 50 5.171233 1
## 580 #00BA38 2 0.25870778 0.35341933 0.35341933 12.935389 50 5.175342 1
## 581 #00BA38 2 0.26099754 0.35654736 0.35654736 13.049877 50 5.179452 1
## 582 #00BA38 2 0.26336394 0.35978008 0.35978008 13.168197 50 5.183562 1
## 583 #00BA38 2 0.26575412 0.36304530 0.36304530 13.287706 50 5.187671 1
## 584 #00BA38 2 0.26824439 0.36644724 0.36644724 13.412219 50 5.191781 1
## 585 #00BA38 2 0.27075527 0.36987734 0.36987734 13.537763 50 5.195890 1
## 586 #00BA38 2 0.27335949 0.37343495 0.37343495 13.667974 50 5.200000 1
## 587 #00BA38 2 0.27600157 0.37704429 0.37704429 13.800079 50 5.204110 1
## 588 #00BA38 2 0.27871874 0.38075620 0.38075620 13.935937 50 5.208219 1
## 589 #00BA38 2 0.28149712 0.38455173 0.38455173 14.074856 50 5.212329 1
## 590 #00BA38 2 0.28433067 0.38842263 0.38842263 14.216534 50 5.216438 1
## 591 #00BA38 2 0.28724981 0.39241045 0.39241045 14.362490 50 5.220548 1
## 592 #00BA38 2 0.29020278 0.39644448 0.39644448 14.510139 50 5.224658 1
## 593 #00BA38 2 0.29326644 0.40062973 0.40062973 14.663322 50 5.228767 1
## 594 #00BA38 2 0.29635356 0.40484704 0.40484704 14.817678 50 5.232877 1
## 595 #00BA38 2 0.29955262 0.40921725 0.40921725 14.977631 50 5.236986 1
## 596 #00BA38 2 0.30278797 0.41363705 0.41363705 15.139399 50 5.241096 1
## 597 #00BA38 2 0.30611273 0.41817898 0.41817898 15.305637 50 5.245205 1
## 598 #00BA38 2 0.30949827 0.42280396 0.42280396 15.474914 50 5.249315 1
## 599 #00BA38 2 0.31294982 0.42751909 0.42751909 15.647491 50 5.253425 1
## 600 #00BA38 2 0.31648668 0.43235078 0.43235078 15.824334 50 5.257534 1
## 601 #00BA38 2 0.32006555 0.43723985 0.43723985 16.003277 50 5.261644 1
## 602 #00BA38 2 0.32375400 0.44227863 0.44227863 16.187700 50 5.265753 1
## 603 #00BA38 2 0.32746671 0.44735054 0.44735054 16.373335 50 5.269863 1
## 604 #00BA38 2 0.33129960 0.45258663 0.45258663 16.564980 50 5.273973 1
## 605 #00BA38 2 0.33516289 0.45786426 0.45786426 16.758145 50 5.278082 1
## 606 #00BA38 2 0.33912132 0.46327185 0.46327185 16.956066 50 5.282192 1
## 607 #00BA38 2 0.34313333 0.46875263 0.46875263 17.156667 50 5.286301 1
## 608 #00BA38 2 0.34721553 0.47432930 0.47432930 17.360777 50 5.290411 1
## 609 #00BA38 2 0.35137344 0.48000940 0.48000940 17.568672 50 5.294521 1
## 610 #00BA38 2 0.35557704 0.48575192 0.48575192 17.778852 50 5.298630 1
## 611 #00BA38 2 0.35987708 0.49162618 0.49162618 17.993854 50 5.302740 1
## 612 #00BA38 2 0.36419970 0.49753128 0.49753128 18.209985 50 5.306849 1
## 613 #00BA38 2 0.36863660 0.50359251 0.50359251 18.431830 50 5.310959 1
## 614 #00BA38 2 0.37309518 0.50968335 0.50968335 18.654759 50 5.315068 1
## 615 #00BA38 2 0.37764279 0.51589582 0.51589582 18.882140 50 5.319178 1
## 616 #00BA38 2 0.38223136 0.52216423 0.52216423 19.111568 50 5.323288 1
## 617 #00BA38 2 0.38688494 0.52852147 0.52852147 19.344247 50 5.327397 1
## 618 #00BA38 2 0.39159661 0.53495806 0.53495806 19.579831 50 5.331507 1
## 619 #00BA38 2 0.39635085 0.54145280 0.54145280 19.817542 50 5.335616 1
## 620 #00BA38 2 0.40117785 0.54804694 0.54804694 20.058893 50 5.339726 1
## 621 #00BA38 2 0.40602687 0.55467116 0.55467116 20.301343 50 5.343836 1
## 622 #00BA38 2 0.41096058 0.56141107 0.56141107 20.548029 50 5.347945 1
## 623 #00BA38 2 0.41591011 0.56817260 0.56817260 20.795505 50 5.352055 1
## 624 #00BA38 2 0.42092892 0.57502878 0.57502878 21.046446 50 5.356164 1
## 625 #00BA38 2 0.42597290 0.58191932 0.58191932 21.298645 50 5.360274 1
## 626 #00BA38 2 0.43106573 0.58887662 0.58887662 21.553287 50 5.364384 1
## 627 #00BA38 2 0.43619366 0.59588185 0.59588185 21.809683 50 5.368493 1
## 628 #00BA38 2 0.44135263 0.60292949 0.60292949 22.067631 50 5.372603 1
## 629 #00BA38 2 0.44655330 0.61003410 0.61003410 22.327665 50 5.376712 1
## 630 #00BA38 2 0.45177009 0.61716073 0.61716073 22.588505 50 5.380822 1
## 631 #00BA38 2 0.45703167 0.62434854 0.62434854 22.851583 50 5.384932 1
## 632 #00BA38 2 0.46230168 0.63154788 0.63154788 23.115084 50 5.389041 1
## 633 #00BA38 2 0.46760761 0.63879629 0.63879629 23.380381 50 5.393151 1
## 634 #00BA38 2 0.47292254 0.64605698 0.64605698 23.646127 50 5.397260 1
## 635 #00BA38 2 0.47825911 0.65334725 0.65334725 23.912956 50 5.401370 1
## 636 #00BA38 2 0.48360584 0.66065138 0.66065138 24.180292 50 5.405479 1
## 637 #00BA38 2 0.48896338 0.66797029 0.66797029 24.448169 50 5.409589 1
## 638 #00BA38 2 0.49432838 0.67529939 0.67529939 24.716419 50 5.413699 1
## 639 #00BA38 2 0.49969698 0.68263340 0.68263340 24.984849 50 5.417808 1
## 640 #00BA38 2 0.50506639 0.68996852 0.68996852 25.253319 50 5.421918 1
## 641 #00BA38 2 0.51043540 0.69730311 0.69730311 25.521770 50 5.426027 1
## 642 #00BA38 2 0.51579563 0.70462569 0.70462569 25.789782 50 5.430137 1
## 643 #00BA38 2 0.52115101 0.71194164 0.71194164 26.057551 50 5.434247 1
## 644 #00BA38 2 0.52649158 0.71923736 0.71923736 26.324579 50 5.438356 1
## 645 #00BA38 2 0.53181899 0.72651511 0.72651511 26.590950 50 5.442466 1
## 646 #00BA38 2 0.53712949 0.73376976 0.73376976 26.856474 50 5.446575 1
## 647 #00BA38 2 0.54241456 0.74098967 0.74098967 27.120728 50 5.450685 1
## 648 #00BA38 2 0.54768458 0.74818901 0.74818901 27.384229 50 5.454795 1
## 649 #00BA38 2 0.55291297 0.75533149 0.75533149 27.645649 50 5.458904 1
## 650 #00BA38 2 0.55813157 0.76246059 0.76246059 27.906578 50 5.463014 1
## 651 #00BA38 2 0.56328964 0.76950700 0.76950700 28.164482 50 5.467123 1
## 652 #00BA38 2 0.56843344 0.77653392 0.77653392 28.421672 50 5.471233 1
## 653 #00BA38 2 0.57352027 0.78348302 0.78348302 28.676014 50 5.475342 1
## 654 #00BA38 2 0.57857537 0.79038876 0.79038876 28.928769 50 5.479452 1
## 655 #00BA38 2 0.58358103 0.79722696 0.79722696 29.179051 50 5.483562 1
## 656 #00BA38 2 0.58853383 0.80399295 0.80399295 29.426691 50 5.487671 1
## 657 #00BA38 2 0.59344861 0.81070701 0.81070701 29.672430 50 5.491781 1
## 658 #00BA38 2 0.59828593 0.81731524 0.81731524 29.914296 50 5.495890 1
## 659 #00BA38 2 0.60310041 0.82389229 0.82389229 30.155021 50 5.500000 1
## 660 #00BA38 2 0.60780955 0.83032542 0.83032542 30.390478 50 5.504110 1
## 661 #00BA38 2 0.61249649 0.83672822 0.83672822 30.624824 50 5.508219 1
## 662 #00BA38 2 0.61708347 0.84299446 0.84299446 30.854173 50 5.512329 1
## 663 #00BA38 2 0.62162689 0.84920121 0.84920121 31.081345 50 5.516438 1
## 664 #00BA38 2 0.62608743 0.85529473 0.85529473 31.304372 50 5.520548 1
## 665 #00BA38 2 0.63047609 0.86129006 0.86129006 31.523805 50 5.524658 1
## 666 #00BA38 2 0.63480231 0.86720009 0.86720009 31.740116 50 5.528767 1
## 667 #00BA38 2 0.63902568 0.87296960 0.87296960 31.951284 50 5.532877 1
## 668 #00BA38 2 0.64321018 0.87868603 0.87868603 32.160509 50 5.536986 1
## 669 #00BA38 2 0.64725848 0.88421639 0.88421639 32.362924 50 5.541096 1
## 670 #00BA38 2 0.65127777 0.88970712 0.88970712 32.563889 50 5.545205 1
## 671 #00BA38 2 0.65515871 0.89500885 0.89500885 32.757936 50 5.549315 1
## 672 #00BA38 2 0.65899177 0.90024517 0.90024517 32.949588 50 5.553425 1
## 673 #00BA38 2 0.66271196 0.90532730 0.90532730 33.135598 50 5.557534 1
## 674 #00BA38 2 0.66635107 0.91029868 0.91029868 33.317554 50 5.561644 1
## 675 #00BA38 2 0.66990532 0.91515412 0.91515412 33.495266 50 5.565753 1
## 676 #00BA38 2 0.67334372 0.91985130 0.91985130 33.667186 50 5.569863 1
## 677 #00BA38 2 0.67672743 0.92447376 0.92447376 33.836371 50 5.573973 1
## 678 #00BA38 2 0.67995930 0.92888881 0.92888881 33.997965 50 5.578082 1
## 679 #00BA38 2 0.68315770 0.93325812 0.93325812 34.157885 50 5.582192 1
## 680 #00BA38 2 0.68618901 0.93739919 0.93739919 34.309451 50 5.586301 1
## 681 #00BA38 2 0.68917494 0.94147824 0.94147824 34.458747 50 5.590411 1
## 682 #00BA38 2 0.69202568 0.94537263 0.94537263 34.601284 50 5.594521 1
## 683 #00BA38 2 0.69479569 0.94915671 0.94915671 34.739784 50 5.598630 1
## 684 #00BA38 2 0.69746378 0.95280159 0.95280159 34.873189 50 5.602740 1
## 685 #00BA38 2 0.70001542 0.95628736 0.95628736 35.000771 50 5.606849 1
## 686 #00BA38 2 0.70249945 0.95968079 0.95968079 35.124973 50 5.610959 1
## 687 #00BA38 2 0.70483127 0.96286627 0.96286627 35.241564 50 5.615068 1
## 688 #00BA38 2 0.70712784 0.96600360 0.96600360 35.356392 50 5.619178 1
## 689 #00BA38 2 0.70924201 0.96889177 0.96889177 35.462101 50 5.623288 1
## 690 #00BA38 2 0.71131839 0.97172830 0.97172830 35.565920 50 5.627397 1
## 691 #00BA38 2 0.71324803 0.97436436 0.97436436 35.662402 50 5.631507 1
## 692 #00BA38 2 0.71510510 0.97690130 0.97690130 35.755255 50 5.635616 1
## 693 #00BA38 2 0.71685127 0.97928673 0.97928673 35.842564 50 5.639726 1
## 694 #00BA38 2 0.71849086 0.98152657 0.98152657 35.924543 50 5.643836 1
## 695 #00BA38 2 0.72005522 0.98366363 0.98366363 36.002761 50 5.647945 1
## 696 #00BA38 2 0.72148006 0.98561009 0.98561009 36.074003 50 5.652055 1
## 697 #00BA38 2 0.72286483 0.98750183 0.98750183 36.143242 50 5.656164 1
## 698 #00BA38 2 0.72407852 0.98915984 0.98915984 36.203926 50 5.660274 1
## 699 #00BA38 2 0.72525901 0.99077250 0.99077250 36.262951 50 5.664384 1
## 700 #00BA38 2 0.72629344 0.99218563 0.99218563 36.314672 50 5.668493 1
## 701 #00BA38 2 0.72726863 0.99351783 0.99351783 36.363431 50 5.672603 1
## 702 #00BA38 2 0.72813330 0.99469905 0.99469905 36.406665 50 5.676712 1
## 703 #00BA38 2 0.72890863 0.99575823 0.99575823 36.445432 50 5.680822 1
## 704 #00BA38 2 0.72960777 0.99671331 0.99671331 36.480388 50 5.684932 1
## 705 #00BA38 2 0.73018939 0.99750787 0.99750787 36.509470 50 5.689041 1
## 706 #00BA38 2 0.73072764 0.99824316 0.99824316 36.536382 50 5.693151 1
## 707 #00BA38 2 0.73112233 0.99878235 0.99878235 36.556116 50 5.697260 1
## 708 #00BA38 2 0.73148790 0.99928175 0.99928175 36.574395 50 5.701370 1
## 709 #00BA38 2 0.73171980 0.99959856 0.99959856 36.585990 50 5.705479 1
## 710 #00BA38 2 0.73190792 0.99985554 0.99985554 36.595396 50 5.709589 1
## 711 #00BA38 2 0.73199503 0.99997454 0.99997454 36.599751 50 5.713699 1
## 712 #00BA38 2 0.73201367 1.00000000 1.00000000 36.600683 50 5.717808 1
## 713 #00BA38 2 0.73196193 0.99992933 0.99992933 36.598097 50 5.721918 1
## 714 #00BA38 2 0.73181948 0.99973473 0.99973473 36.590974 50 5.726027 1
## 715 #00BA38 2 0.73163509 0.99948283 0.99948283 36.581754 50 5.730137 1
## 716 #00BA38 2 0.73134024 0.99908004 0.99908004 36.567012 50 5.734247 1
## 717 #00BA38 2 0.73102174 0.99864494 0.99864494 36.551087 50 5.738356 1
## 718 #00BA38 2 0.73059128 0.99805688 0.99805688 36.529564 50 5.742466 1
## 719 #00BA38 2 0.73013185 0.99742926 0.99742926 36.506592 50 5.746575 1
## 720 #00BA38 2 0.72958821 0.99668660 0.99668660 36.479411 50 5.750685 1
## 721 #00BA38 2 0.72899702 0.99587898 0.99587898 36.449851 50 5.754795 1
## 722 #00BA38 2 0.72834691 0.99499086 0.99499086 36.417345 50 5.758904 1
## 723 #00BA38 2 0.72763320 0.99401587 0.99401587 36.381660 50 5.763014 1
## 724 #00BA38 2 0.72688331 0.99299144 0.99299144 36.344165 50 5.767123 1
## 725 #00BA38 2 0.72605633 0.99186172 0.99186172 36.302817 50 5.771233 1
## 726 #00BA38 2 0.72521173 0.99070791 0.99070791 36.260586 50 5.775342 1
## 727 #00BA38 2 0.72428229 0.98943820 0.98943820 36.214114 50 5.779452 1
## 728 #00BA38 2 0.72333611 0.98814564 0.98814564 36.166805 50 5.783562 1
## 729 #00BA38 2 0.72232675 0.98676676 0.98676676 36.116338 50 5.787671 1
## 730 #00BA38 2 0.72128799 0.98534772 0.98534772 36.064400 50 5.791781 1
## 731 #00BA38 2 0.72020515 0.98386844 0.98386844 36.010257 50 5.795890 1
## 732 #00BA38 2 0.71908258 0.98233491 0.98233491 35.954129 50 5.800000 1
## 733 #00BA38 2 0.71793250 0.98076379 0.98076379 35.896625 50 5.804110 1
## 734 #00BA38 2 0.71673463 0.97912739 0.97912739 35.836731 50 5.808219 1
## 735 #00BA38 2 0.71552340 0.97747273 0.97747273 35.776170 50 5.812329 1
## 736 #00BA38 2 0.71425840 0.97574463 0.97574463 35.712920 50 5.816438 1
## 737 #00BA38 2 0.71298378 0.97400337 0.97400337 35.649189 50 5.820548 1
## 738 #00BA38 2 0.71166756 0.97220529 0.97220529 35.583378 50 5.824658 1
## 739 #00BA38 2 0.71033567 0.97038580 0.97038580 35.516783 50 5.828767 1
## 740 #00BA38 2 0.70897512 0.96852716 0.96852716 35.448756 50 5.832877 1
## 741 #00BA38 2 0.70759319 0.96663932 0.96663932 35.379660 50 5.836986 1
## 742 #00BA38 2 0.70619337 0.96472703 0.96472703 35.309669 50 5.841096 1
## 743 #00BA38 2 0.70476818 0.96278009 0.96278009 35.238409 50 5.845205 1
## 744 #00BA38 2 0.70333384 0.96082064 0.96082064 35.166692 50 5.849315 1
## 745 #00BA38 2 0.70187166 0.95882317 0.95882317 35.093583 50 5.853425 1
## 746 #00BA38 2 0.70040422 0.95681851 0.95681851 35.020211 50 5.857534 1
## 747 #00BA38 2 0.69891383 0.95478249 0.95478249 34.945692 50 5.861644 1
## 748 #00BA38 2 0.69741662 0.95273716 0.95273716 34.870831 50 5.865753 1
## 749 #00BA38 2 0.69590402 0.95067081 0.95067081 34.795201 50 5.869863 1
## 750 #00BA38 2 0.69438206 0.94859167 0.94859167 34.719103 50 5.873973 1
## 751 #00BA38 2 0.69285067 0.94649963 0.94649963 34.642533 50 5.878082 1
## 752 #00BA38 2 0.69130844 0.94439280 0.94439280 34.565422 50 5.882192 1
## 753 #00BA38 2 0.68976130 0.94227927 0.94227927 34.488065 50 5.886301 1
## 754 #00BA38 2 0.68820272 0.94015010 0.94015010 34.410136 50 5.890411 1
## 755 #00BA38 2 0.68664181 0.93801775 0.93801775 34.332091 50 5.894521 1
## 756 #00BA38 2 0.68507099 0.93587185 0.93587185 34.253549 50 5.898630 1
## 757 #00BA38 2 0.68349790 0.93372287 0.93372287 34.174895 50 5.902740 1
## 758 #00BA38 2 0.68191841 0.93156514 0.93156514 34.095921 50 5.906849 1
## 759 #00BA38 2 0.68033577 0.92940310 0.92940310 34.016789 50 5.910959 1
## 760 #00BA38 2 0.67874929 0.92723582 0.92723582 33.937465 50 5.915068 1
## 761 #00BA38 2 0.67715919 0.92506359 0.92506359 33.857959 50 5.919178 1
## 762 #00BA38 2 0.67556706 0.92288859 0.92288859 33.778353 50 5.923288 1
## 763 #00BA38 2 0.67397107 0.92070831 0.92070831 33.698553 50 5.927397 1
## 764 #00BA38 2 0.67237421 0.91852686 0.91852686 33.618711 50 5.931507 1
## 765 #00BA38 2 0.67077353 0.91634017 0.91634017 33.538676 50 5.935616 1
## 766 #00BA38 2 0.66917209 0.91415246 0.91415246 33.458605 50 5.939726 1
## 767 #00BA38 2 0.66756791 0.91196099 0.91196099 33.378395 50 5.943836 1
## 768 #00BA38 2 0.66596237 0.90976767 0.90976767 33.298118 50 5.947945 1
## 769 #00BA38 2 0.66435483 0.90757163 0.90757163 33.217742 50 5.952055 1
## 770 #00BA38 2 0.66274524 0.90537277 0.90537277 33.137262 50 5.956164 1
## 771 #00BA38 2 0.66113424 0.90317199 0.90317199 33.056712 50 5.960274 1
## 772 #00BA38 2 0.65952026 0.90096714 0.90096714 32.976013 50 5.964384 1
## 773 #00BA38 2 0.65790547 0.89876118 0.89876118 32.895273 50 5.968493 1
## 774 #00BA38 2 0.65628642 0.89654940 0.89654940 32.814321 50 5.972603 1
## 775 #00BA38 2 0.65466641 0.89433632 0.89433632 32.733321 50 5.976712 1
## 776 #00BA38 2 0.65304216 0.89211744 0.89211744 32.652108 50 5.980822 1
## 777 #00BA38 2 0.65141575 0.88989561 0.88989561 32.570787 50 5.984932 1
## 778 #00BA38 2 0.64978548 0.88766851 0.88766851 32.489274 50 5.989041 1
## 779 #00BA38 2 0.64815132 0.88543609 0.88543609 32.407566 50 5.993151 1
## 780 #00BA38 2 0.64651397 0.88319932 0.88319932 32.325698 50 5.997260 1
## 781 #00BA38 2 0.64487051 0.88095419 0.88095419 32.243525 50 6.001370 1
## 782 #00BA38 2 0.64322489 0.87870613 0.87870613 32.161245 50 6.005479 1
## 783 #00BA38 2 0.64157040 0.87644594 0.87644594 32.078520 50 6.009589 1
## 784 #00BA38 2 0.63991393 0.87418303 0.87418303 31.995696 50 6.013699 1
## 785 #00BA38 2 0.63824787 0.87190704 0.87190704 31.912393 50 6.017808 1
## 786 #00BA38 2 0.63657798 0.86962581 0.86962581 31.828899 50 6.021918 1
## 787 #00BA38 2 0.63489961 0.86733301 0.86733301 31.744981 50 6.026027 1
## 788 #00BA38 2 0.63321435 0.86503077 0.86503077 31.660717 50 6.030137 1
## 789 #00BA38 2 0.63152224 0.86271920 0.86271920 31.576112 50 6.034247 1
## 790 #00BA38 2 0.62981962 0.86039325 0.86039325 31.490981 50 6.038356 1
## 791 #00BA38 2 0.62811233 0.85806093 0.85806093 31.405617 50 6.042466 1
## 792 #00BA38 2 0.62639037 0.85570857 0.85570857 31.319518 50 6.046575 1
## 793 #00BA38 2 0.62466514 0.85335174 0.85335174 31.233257 50 6.050685 1
## 794 #00BA38 2 0.62292325 0.85097216 0.85097216 31.146162 50 6.054795 1
## 795 #00BA38 2 0.62117623 0.84858557 0.84858557 31.058812 50 6.058904 1
## 796 #00BA38 2 0.61941502 0.84617958 0.84617958 30.970751 50 6.063014 1
## 797 #00BA38 2 0.61764442 0.84376077 0.84376077 30.882221 50 6.067123 1
## 798 #00BA38 2 0.61586261 0.84132666 0.84132666 30.793131 50 6.071233 1
## 799 #00BA38 2 0.61406674 0.83887333 0.83887333 30.703337 50 6.075342 1
## 800 #00BA38 2 0.61226318 0.83640950 0.83640950 30.613159 50 6.079452 1
## 801 #00BA38 2 0.61044053 0.83391957 0.83391957 30.522026 50 6.083562 1
## 802 #00BA38 2 0.60861349 0.83142367 0.83142367 30.430674 50 6.087671 1
## 803 #00BA38 2 0.60676336 0.82889622 0.82889622 30.338168 50 6.091781 1
## 804 #00BA38 2 0.60490787 0.82636144 0.82636144 30.245393 50 6.095890 1
## 805 #00BA38 2 0.60303318 0.82380044 0.82380044 30.151659 50 6.100000 1
## 806 #00BA38 2 0.60114818 0.82122535 0.82122535 30.057409 50 6.104110 1
## 807 #00BA38 2 0.59924828 0.81862990 0.81862990 29.962414 50 6.108219 1
## 808 #00BA38 2 0.59733294 0.81601338 0.81601338 29.866647 50 6.112329 1
## 809 #00BA38 2 0.59540732 0.81338279 0.81338279 29.770366 50 6.116438 1
## 810 #00BA38 2 0.59346109 0.81072406 0.81072406 29.673055 50 6.120548 1
## 811 #00BA38 2 0.59150940 0.80805787 0.80805787 29.575470 50 6.124658 1
## 812 #00BA38 2 0.58953197 0.80535651 0.80535651 29.476599 50 6.128767 1
## 813 #00BA38 2 0.58754954 0.80264832 0.80264832 29.377477 50 6.132877 1
## 814 #00BA38 2 0.58554539 0.79991046 0.79991046 29.277269 50 6.136986 1
## 815 #00BA38 2 0.58353179 0.79715969 0.79715969 29.176589 50 6.141096 1
## 816 #00BA38 2 0.58150158 0.79438624 0.79438624 29.075079 50 6.145205 1
## 817 #00BA38 2 0.57945716 0.79159336 0.79159336 28.972858 50 6.149315 1
## 818 #00BA38 2 0.57740126 0.78878480 0.78878480 28.870063 50 6.153425 1
## 819 #00BA38 2 0.57532664 0.78595068 0.78595068 28.766332 50 6.157534 1
## 820 #00BA38 2 0.57324558 0.78310776 0.78310776 28.662279 50 6.161644 1
## 821 #00BA38 2 0.57114168 0.78023362 0.78023362 28.557084 50 6.165753 1
## 822 #00BA38 2 0.56903322 0.77735328 0.77735328 28.451661 50 6.169863 1
## 823 #00BA38 2 0.56690417 0.77444479 0.77444479 28.345208 50 6.173973 1
## 824 #00BA38 2 0.56476800 0.77152658 0.77152658 28.238400 50 6.178082 1
## 825 #00BA38 2 0.56261645 0.76858736 0.76858736 28.130823 50 6.182192 1
## 826 #00BA38 2 0.56045412 0.76563342 0.76563342 28.022706 50 6.186301 1
## 827 #00BA38 2 0.55828130 0.76266514 0.76266514 27.914065 50 6.190411 1
## 828 #00BA38 2 0.55609461 0.75967790 0.75967790 27.804730 50 6.194521 1
## 829 #00BA38 2 0.55390190 0.75668245 0.75668245 27.695095 50 6.198630 1
## 830 #00BA38 2 0.55169288 0.75366472 0.75366472 27.584644 50 6.202740 1
## 831 #00BA38 2 0.54948048 0.75064239 0.75064239 27.474024 50 6.206849 1
## 832 #00BA38 2 0.54725272 0.74759905 0.74759905 27.362636 50 6.210959 1
## 833 #00BA38 2 0.54502094 0.74455023 0.74455023 27.251047 50 6.215068 1
## 834 #00BA38 2 0.54277829 0.74148655 0.74148655 27.138914 50 6.219178 1
## 835 #00BA38 2 0.54052966 0.73841471 0.73841471 27.026483 50 6.223288 1
## 836 #00BA38 2 0.53827403 0.73533331 0.73533331 26.913701 50 6.227397 1
## 837 #00BA38 2 0.53601127 0.73224217 0.73224217 26.800564 50 6.231507 1
## 838 #00BA38 2 0.53374470 0.72914581 0.72914581 26.687235 50 6.235616 1
## 839 #00BA38 2 0.53147070 0.72603931 0.72603931 26.573535 50 6.239726 1
## 840 #00BA38 2 0.52919508 0.72293060 0.72293060 26.459754 50 6.243836 1
## 841 #00BA38 2 0.52691305 0.71981314 0.71981314 26.345653 50 6.247945 1
## 842 #00BA38 2 0.52463006 0.71669435 0.71669435 26.231503 50 6.252055 1
## 843 #00BA38 2 0.52234367 0.71357093 0.71357093 26.117184 50 6.256164 1
## 844 #00BA38 2 0.52005645 0.71044636 0.71044636 26.002822 50 6.260274 1
## 845 #00BA38 2 0.51776803 0.70732017 0.70732017 25.888401 50 6.264384 1
## 846 #00BA38 2 0.51547981 0.70419424 0.70419424 25.773990 50 6.268493 1
## 847 #00BA38 2 0.51319171 0.70106849 0.70106849 25.659586 50 6.272603 1
## 848 #00BA38 2 0.51090577 0.69794568 0.69794568 25.545289 50 6.276712 1
## 849 #00BA38 2 0.50862039 0.69482362 0.69482362 25.431019 50 6.280822 1
## 850 #00BA38 2 0.50634003 0.69170844 0.69170844 25.317001 50 6.284932 1
## 851 #00BA38 2 0.50406107 0.68859516 0.68859516 25.203053 50 6.289041 1
## 852 #00BA38 2 0.50178826 0.68549030 0.68549030 25.089413 50 6.293151 1
## 853 #00BA38 2 0.49951908 0.68239038 0.68239038 24.975954 50 6.297260 1
## 854 #00BA38 2 0.49725613 0.67929897 0.67929897 24.862806 50 6.301370 1
## 855 #00BA38 2 0.49499997 0.67621684 0.67621684 24.749998 50 6.305479 1
## 856 #00BA38 2 0.49274919 0.67314207 0.67314207 24.637460 50 6.309589 1
## 857 #00BA38 2 0.49050923 0.67008207 0.67008207 24.525462 50 6.313699 1
## 858 #00BA38 2 0.48827291 0.66702704 0.66702704 24.413645 50 6.317808 1
## 859 #00BA38 2 0.48605223 0.66399338 0.66399338 24.302611 50 6.321918 1
## 860 #00BA38 2 0.48383500 0.66096444 0.66096444 24.191750 50 6.326027 1
## 861 #00BA38 2 0.48163412 0.65795783 0.65795783 24.081706 50 6.330137 1
## 862 #00BA38 2 0.47943992 0.65496035 0.65496035 23.971996 50 6.334247 1
## 863 #00BA38 2 0.47725987 0.65198219 0.65198219 23.862994 50 6.338356 1
## 864 #00BA38 2 0.47509145 0.64901992 0.64901992 23.754573 50 6.342466 1
## 865 #00BA38 2 0.47293415 0.64607284 0.64607284 23.646707 50 6.346575 1
## 866 #00BA38 2 0.47079409 0.64314932 0.64314932 23.539704 50 6.350685 1
## 867 #00BA38 2 0.46866135 0.64023579 0.64023579 23.433067 50 6.354795 1
## 868 #00BA38 2 0.46655203 0.63735426 0.63735426 23.327601 50 6.358904 1
## 869 #00BA38 2 0.46444784 0.63447974 0.63447974 23.222392 50 6.363014 1
## 870 #00BA38 2 0.46236911 0.63164000 0.63164000 23.118455 50 6.367123 1
## 871 #00BA38 2 0.46029837 0.62881117 0.62881117 23.014918 50 6.371233 1
## 872 #00BA38 2 0.45824879 0.62601125 0.62601125 22.912439 50 6.375342 1
## 873 #00BA38 2 0.45621333 0.62323061 0.62323061 22.810666 50 6.379452 1
## 874 #00BA38 2 0.45419411 0.62047218 0.62047218 22.709706 50 6.383562 1
## 875 #00BA38 2 0.45219550 0.61774188 0.61774188 22.609775 50 6.387671 1
## 876 #00BA38 2 0.45020771 0.61502637 0.61502637 22.510385 50 6.391781 1
## 877 #00BA38 2 0.44824724 0.61234818 0.61234818 22.412362 50 6.395890 1
## 878 #00BA38 2 0.44629295 0.60967844 0.60967844 22.314647 50 6.400000 1
## 879 #00BA38 2 0.44437041 0.60705207 0.60705207 22.218521 50 6.404110 1
## 880 #00BA38 2 0.44245549 0.60443611 0.60443611 22.122775 50 6.408219 1
## 881 #00BA38 2 0.44056641 0.60185545 0.60185545 22.028321 50 6.412329 1
## 882 #00BA38 2 0.43869146 0.59929408 0.59929408 21.934573 50 6.416438 1
## 883 #00BA38 2 0.43683614 0.59675954 0.59675954 21.841807 50 6.420548 1
## 884 #00BA38 2 0.43500143 0.59425316 0.59425316 21.750072 50 6.424658 1
## 885 #00BA38 2 0.43317997 0.59176486 0.59176486 21.658998 50 6.428767 1
## 886 #00BA38 2 0.43138549 0.58931344 0.58931344 21.569275 50 6.432877 1
## 887 #00BA38 2 0.42959778 0.58687125 0.58687125 21.479889 50 6.436986 1
## 888 #00BA38 2 0.42784319 0.58447432 0.58447432 21.392159 50 6.441096 1
## 889 #00BA38 2 0.42609489 0.58208597 0.58208597 21.304744 50 6.445205 1
## 890 #00BA38 2 0.42437356 0.57973447 0.57973447 21.218678 50 6.449315 1
## 891 #00BA38 2 0.42266422 0.57739935 0.57739935 21.133211 50 6.453425 1
## 892 #00BA38 2 0.42097512 0.57509189 0.57509189 21.048756 50 6.457534 1
## 893 #00BA38 2 0.41930366 0.57280851 0.57280851 20.965183 50 6.461644 1
## 894 #00BA38 2 0.41764590 0.57054386 0.57054386 20.882295 50 6.465753 1
## 895 #00BA38 2 0.41601092 0.56831031 0.56831031 20.800546 50 6.469863 1
## 896 #00BA38 2 0.41438340 0.56608697 0.56608697 20.719170 50 6.473973 1
## 897 #00BA38 2 0.41278321 0.56390096 0.56390096 20.639160 50 6.478082 1
## 898 #00BA38 2 0.41118834 0.56172222 0.56172222 20.559417 50 6.482192 1
## 899 #00BA38 2 0.40961726 0.55957598 0.55957598 20.480863 50 6.486301 1
## 900 #00BA38 2 0.40805460 0.55744123 0.55744123 20.402730 50 6.490411 1
## 901 #00BA38 2 0.40650937 0.55533031 0.55533031 20.325469 50 6.494521 1
## 902 #00BA38 2 0.40497656 0.55323633 0.55323633 20.248828 50 6.498630 1
## 903 #00BA38 2 0.40345538 0.55115826 0.55115826 20.172769 50 6.502740 1
## 904 #00BA38 2 0.40194981 0.54910151 0.54910151 20.097491 50 6.506849 1
## 905 #00BA38 2 0.40045072 0.54705361 0.54705361 20.022536 50 6.510959 1
## 906 #00BA38 2 0.39896956 0.54503021 0.54503021 19.948478 50 6.515068 1
## 907 #00BA38 2 0.39749202 0.54301174 0.54301174 19.874601 50 6.519178 1
## 908 #00BA38 2 0.39603065 0.54101538 0.54101538 19.801532 50 6.523288 1
## 909 #00BA38 2 0.39457357 0.53902487 0.53902487 19.728679 50 6.527397 1
## 910 #00BA38 2 0.39312759 0.53704952 0.53704952 19.656379 50 6.531507 1
## 911 #00BA38 2 0.39168767 0.53508245 0.53508245 19.584383 50 6.535616 1
## 912 #00BA38 2 0.39025459 0.53312474 0.53312474 19.512730 50 6.539726 1
## 913 #00BA38 2 0.38882835 0.53117636 0.53117636 19.441418 50 6.543836 1
## 914 #00BA38 2 0.38740563 0.52923279 0.52923279 19.370282 50 6.547945 1
## 915 #00BA38 2 0.38598947 0.52729817 0.52729817 19.299473 50 6.552055 1
## 916 #00BA38 2 0.38457466 0.52536541 0.52536541 19.228733 50 6.556164 1
## 917 #00BA38 2 0.38316464 0.52343919 0.52343919 19.158232 50 6.560274 1
## 918 #00BA38 2 0.38175514 0.52151368 0.52151368 19.087757 50 6.564384 1
## 919 #00BA38 2 0.38034736 0.51959052 0.51959052 19.017368 50 6.568493 1
## 920 #00BA38 2 0.37893935 0.51766704 0.51766704 18.946967 50 6.572603 1
## 921 #00BA38 2 0.37753101 0.51574312 0.51574312 18.876551 50 6.576712 1
## 922 #00BA38 2 0.37612061 0.51381638 0.51381638 18.806030 50 6.580822 1
## 923 #00BA38 2 0.37470891 0.51188786 0.51188786 18.735445 50 6.584932 1
## 924 #00BA38 2 0.37329224 0.50995255 0.50995255 18.664612 50 6.589041 1
## 925 #00BA38 2 0.37187436 0.50801559 0.50801559 18.593718 50 6.593151 1
## 926 #00BA38 2 0.37044754 0.50606643 0.50606643 18.522377 50 6.597260 1
## 927 #00BA38 2 0.36901849 0.50411422 0.50411422 18.450925 50 6.601370 1
## 928 #00BA38 2 0.36757988 0.50214893 0.50214893 18.378994 50 6.605479 1
## 929 #00BA38 2 0.36613582 0.50017620 0.50017620 18.306791 50 6.609589 1
## 930 #00BA38 2 0.36468272 0.49819114 0.49819114 18.234136 50 6.613699 1
## 931 #00BA38 2 0.36321990 0.49619279 0.49619279 18.160995 50 6.617808 1
## 932 #00BA38 2 0.36174966 0.49418430 0.49418430 18.087483 50 6.621918 1
## 933 #00BA38 2 0.36026446 0.49215538 0.49215538 18.013223 50 6.626027 1
## 934 #00BA38 2 0.35877448 0.49011992 0.49011992 17.938724 50 6.630137 1
## 935 #00BA38 2 0.35726340 0.48805564 0.48805564 17.863170 50 6.634247 1
## 936 #00BA38 2 0.35574774 0.48598511 0.48598511 17.787387 50 6.638356 1
## 937 #00BA38 2 0.35421087 0.48388559 0.48388559 17.710543 50 6.642466 1
## 938 #00BA38 2 0.35266511 0.48177394 0.48177394 17.633255 50 6.646575 1
## 939 #00BA38 2 0.35110129 0.47963762 0.47963762 17.555065 50 6.650685 1
## 940 #00BA38 2 0.34952233 0.47748061 0.47748061 17.476117 50 6.654795 1
## 941 #00BA38 2 0.34792942 0.47530455 0.47530455 17.396471 50 6.658904 1
## 942 #00BA38 2 0.34631438 0.47309824 0.47309824 17.315719 50 6.663014 1
## 943 #00BA38 2 0.34469035 0.47087966 0.47087966 17.234517 50 6.667123 1
## 944 #00BA38 2 0.34303656 0.46862043 0.46862043 17.151828 50 6.671233 1
## 945 #00BA38 2 0.34137631 0.46635237 0.46635237 17.068816 50 6.675342 1
## 946 #00BA38 2 0.33968457 0.46404129 0.46404129 16.984228 50 6.679452 1
## 947 #00BA38 2 0.33798255 0.46171617 0.46171617 16.899127 50 6.683562 1
## 948 #00BA38 2 0.33625454 0.45935555 0.45935555 16.812727 50 6.687671 1
## 949 #00BA38 2 0.33450868 0.45697054 0.45697054 16.725434 50 6.691781 1
## 950 #00BA38 2 0.33274302 0.45455848 0.45455848 16.637151 50 6.695890 1
## 951 #00BA38 2 0.33095154 0.45211115 0.45211115 16.547577 50 6.700000 1
## 952 #00BA38 2 0.32914703 0.44964601 0.44964601 16.457351 50 6.704110 1
## 953 #00BA38 2 0.32730841 0.44713429 0.44713429 16.365421 50 6.708219 1
## 954 #00BA38 2 0.32546215 0.44461213 0.44461213 16.273108 50 6.712329 1
## 955 #00BA38 2 0.32357707 0.44203693 0.44203693 16.178854 50 6.716438 1
## 956 #00BA38 2 0.32168220 0.43944835 0.43944835 16.084110 50 6.720548 1
## 957 #00BA38 2 0.31975577 0.43681667 0.43681667 15.987789 50 6.724658 1
## 958 #00BA38 2 0.31781148 0.43416058 0.43416058 15.890574 50 6.728767 1
## 959 #00BA38 2 0.31584326 0.43147180 0.43147180 15.792163 50 6.732877 1
## 960 #00BA38 2 0.31384903 0.42874751 0.42874751 15.692452 50 6.736986 1
## 961 #00BA38 2 0.31183875 0.42600127 0.42600127 15.591938 50 6.741096 1
## 962 #00BA38 2 0.30979440 0.42320849 0.42320849 15.489720 50 6.745205 1
## 963 #00BA38 2 0.30774199 0.42040470 0.42040470 15.387099 50 6.749315 1
## 964 #00BA38 2 0.30564759 0.41754355 0.41754355 15.282379 50 6.753425 1
## 965 #00BA38 2 0.30354523 0.41467154 0.41467154 15.177262 50 6.757534 1
## 966 #00BA38 2 0.30140911 0.41175339 0.41175339 15.070456 50 6.761644 1
## 967 #00BA38 2 0.29925728 0.40881379 0.40881379 14.962864 50 6.765753 1
## 968 #00BA38 2 0.29707994 0.40583933 0.40583933 14.853997 50 6.769863 1
## 969 #00BA38 2 0.29487941 0.40283320 0.40283320 14.743970 50 6.773973 1
## 970 #00BA38 2 0.29266151 0.39980334 0.39980334 14.633075 50 6.778082 1
## 971 #00BA38 2 0.29041333 0.39673211 0.39673211 14.520666 50 6.782192 1
## 972 #00BA38 2 0.28815570 0.39364798 0.39364798 14.407785 50 6.786301 1
## 973 #00BA38 2 0.28586118 0.39051345 0.39051345 14.293059 50 6.790411 1
## 974 #00BA38 2 0.28355944 0.38736905 0.38736905 14.177972 50 6.794521 1
## 975 #00BA38 2 0.28122553 0.38418072 0.38418072 14.061277 50 6.798630 1
## 976 #00BA38 2 0.27887951 0.38097582 0.38097582 13.943975 50 6.802740 1
## 977 #00BA38 2 0.27650936 0.37773797 0.37773797 13.825468 50 6.806849 1
## 978 #00BA38 2 0.27412093 0.37447516 0.37447516 13.706047 50 6.810959 1
## 979 #00BA38 2 0.27171599 0.37118977 0.37118977 13.585799 50 6.815068 1
## 980 #00BA38 2 0.26928727 0.36787192 0.36787192 13.464363 50 6.819178 1
## 981 #00BA38 2 0.26684912 0.36454117 0.36454117 13.342456 50 6.823288 1
## 982 #00BA38 2 0.26438242 0.36117142 0.36117142 13.219121 50 6.827397 1
## 983 #00BA38 2 0.26190989 0.35779372 0.35779372 13.095495 50 6.831507 1
## 984 #00BA38 2 0.25941059 0.35437943 0.35437943 12.970529 50 6.835616 1
## 985 #00BA38 2 0.25690330 0.35095424 0.35095424 12.845165 50 6.839726 1
## 986 #00BA38 2 0.25437628 0.34750209 0.34750209 12.718814 50 6.843836 1
## 987 #00BA38 2 0.25183696 0.34403314 0.34403314 12.591848 50 6.847945 1
## 988 #00BA38 2 0.24928425 0.34054589 0.34054589 12.464212 50 6.852055 1
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## 1412 #619CFF 3 0.29410995 0.41234967 0.41234967 14.705497 50 7.172016 1
## 1413 #619CFF 3 0.29249546 0.41008611 0.41008611 14.624773 50 7.177886 1
## 1414 #619CFF 3 0.29090629 0.40785806 0.40785806 14.545314 50 7.183757 1
## 1415 #619CFF 3 0.28933214 0.40565106 0.40565106 14.466607 50 7.189628 1
## 1416 #619CFF 3 0.28778426 0.40348090 0.40348090 14.389213 50 7.195499 1
## 1417 #619CFF 3 0.28625493 0.40133673 0.40133673 14.312747 50 7.201370 1
## 1418 #619CFF 3 0.28473874 0.39921099 0.39921099 14.236937 50 7.207241 1
## 1419 #619CFF 3 0.28324845 0.39712156 0.39712156 14.162422 50 7.213112 1
## 1420 #619CFF 3 0.28177197 0.39505151 0.39505151 14.088599 50 7.218982 1
## 1421 #619CFF 3 0.28030831 0.39299942 0.39299942 14.015416 50 7.224853 1
## 1422 #619CFF 3 0.27886968 0.39098242 0.39098242 13.943484 50 7.230724 1
## 1423 #619CFF 3 0.27744256 0.38898156 0.38898156 13.872128 50 7.236595 1
## 1424 #619CFF 3 0.27603052 0.38700185 0.38700185 13.801526 50 7.242466 1
## 1425 #619CFF 3 0.27464092 0.38505359 0.38505359 13.732046 50 7.248337 1
## 1426 #619CFF 3 0.27326325 0.38312207 0.38312207 13.663163 50 7.254207 1
## 1427 #619CFF 3 0.27190426 0.38121673 0.38121673 13.595213 50 7.260078 1
## 1428 #619CFF 3 0.27056667 0.37934139 0.37934139 13.528334 50 7.265949 1
## 1429 #619CFF 3 0.26924215 0.37748437 0.37748437 13.462107 50 7.271820 1
## 1430 #619CFF 3 0.26794176 0.37566120 0.37566120 13.397088 50 7.277691 1
## 1431 #619CFF 3 0.26666251 0.37386766 0.37386766 13.333125 50 7.283562 1
## 1432 #619CFF 3 0.26539807 0.37209489 0.37209489 13.269904 50 7.289432 1
## 1433 #619CFF 3 0.26416553 0.37036682 0.37036682 13.208276 50 7.295303 1
## 1434 #619CFF 3 0.26295379 0.36866793 0.36866793 13.147689 50 7.301174 1
## 1435 #619CFF 3 0.26175955 0.36699358 0.36699358 13.087978 50 7.307045 1
## 1436 #619CFF 3 0.26060654 0.36537703 0.36537703 13.030327 50 7.312916 1
## 1437 #619CFF 3 0.25947367 0.36378871 0.36378871 12.973683 50 7.318787 1
## 1438 #619CFF 3 0.25836619 0.36223600 0.36223600 12.918309 50 7.324658 1
## 1439 #619CFF 3 0.25730184 0.36074375 0.36074375 12.865092 50 7.330528 1
## 1440 #619CFF 3 0.25626051 0.35928378 0.35928378 12.813025 50 7.336399 1
## 1441 #619CFF 3 0.25525421 0.35787294 0.35787294 12.762711 50 7.342270 1
## 1442 #619CFF 3 0.25429158 0.35652330 0.35652330 12.714579 50 7.348141 1
## 1443 #619CFF 3 0.25335480 0.35520991 0.35520991 12.667740 50 7.354012 1
## 1444 #619CFF 3 0.25246412 0.35396116 0.35396116 12.623206 50 7.359883 1
## 1445 #619CFF 3 0.25161574 0.35277171 0.35277171 12.580787 50 7.365753 1
## 1446 #619CFF 3 0.25079582 0.35162215 0.35162215 12.539791 50 7.371624 1
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## 1448 #619CFF 3 0.24931064 0.34953989 0.34953989 12.465532 50 7.383366 1
## 1449 #619CFF 3 0.24861810 0.34856894 0.34856894 12.430905 50 7.389237 1
## 1450 #619CFF 3 0.24799514 0.34769552 0.34769552 12.399757 50 7.395108 1
## 1451 #619CFF 3 0.24740552 0.34686886 0.34686886 12.370276 50 7.400978 1
## 1452 #619CFF 3 0.24685477 0.34609670 0.34609670 12.342739 50 7.406849 1
## 1453 #619CFF 3 0.24637060 0.34541788 0.34541788 12.318530 50 7.412720 1
## 1454 #619CFF 3 0.24591930 0.34478515 0.34478515 12.295965 50 7.418591 1
## 1455 #619CFF 3 0.24551543 0.34421890 0.34421890 12.275771 50 7.424462 1
## 1456 #619CFF 3 0.24517040 0.34373516 0.34373516 12.258520 50 7.430333 1
## 1457 #619CFF 3 0.24485786 0.34329697 0.34329697 12.242893 50 7.436204 1
## 1458 #619CFF 3 0.24459943 0.34293466 0.34293466 12.229972 50 7.442074 1
## 1459 #619CFF 3 0.24439004 0.34264109 0.34264109 12.219502 50 7.447945 1
## 1460 #619CFF 3 0.24421169 0.34239104 0.34239104 12.210585 50 7.453816 1
## 1461 #619CFF 3 0.24409119 0.34222209 0.34222209 12.204559 50 7.459687 1
## 1462 #619CFF 3 0.24400861 0.34210630 0.34210630 12.200430 50 7.465558 1
## 1463 #619CFF 3 0.24395452 0.34203047 0.34203047 12.197726 50 7.471429 1
## 1464 #619CFF 3 0.24395788 0.34203518 0.34203518 12.197894 50 7.477299 1
## 1465 #619CFF 3 0.24398778 0.34207711 0.34207711 12.199389 50 7.483170 1
## 1466 #619CFF 3 0.24404576 0.34215840 0.34215840 12.202288 50 7.489041 1
## 1467 #619CFF 3 0.24414917 0.34230338 0.34230338 12.207458 50 7.494912 1
## 1468 #619CFF 3 0.24427180 0.34247530 0.34247530 12.213590 50 7.500783 1
## 1469 #619CFF 3 0.24442045 0.34268372 0.34268372 12.221022 50 7.506654 1
## 1470 #619CFF 3 0.24459771 0.34293224 0.34293224 12.229885 50 7.512524 1
## 1471 #619CFF 3 0.24478821 0.34319933 0.34319933 12.239410 50 7.518395 1
## 1472 #619CFF 3 0.24499794 0.34349337 0.34349337 12.249897 50 7.524266 1
## 1473 #619CFF 3 0.24522053 0.34380546 0.34380546 12.261027 50 7.530137 1
## 1474 #619CFF 3 0.24544955 0.34412655 0.34412655 12.272478 50 7.536008 1
## 1475 #619CFF 3 0.24568574 0.34445769 0.34445769 12.284287 50 7.541879 1
## 1476 #619CFF 3 0.24592130 0.34478795 0.34478795 12.296065 50 7.547750 1
## 1477 #619CFF 3 0.24615577 0.34511669 0.34511669 12.307789 50 7.553620 1
## 1478 #619CFF 3 0.24637991 0.34543094 0.34543094 12.318996 50 7.559491 1
## 1479 #619CFF 3 0.24659318 0.34572994 0.34572994 12.329659 50 7.565362 1
## 1480 #619CFF 3 0.24679600 0.34601431 0.34601431 12.339800 50 7.571233 1
## 1481 #619CFF 3 0.24696842 0.34625604 0.34625604 12.348421 50 7.577104 1
## 1482 #619CFF 3 0.24712237 0.34647188 0.34647188 12.356119 50 7.582975 1
## 1483 #619CFF 3 0.24725114 0.34665241 0.34665241 12.362557 50 7.588845 1
## 1484 #619CFF 3 0.24733494 0.34676991 0.34676991 12.366747 50 7.594716 1
## 1485 #619CFF 3 0.24739203 0.34684996 0.34684996 12.369602 50 7.600587 1
## 1486 #619CFF 3 0.24740483 0.34686790 0.34686790 12.370241 50 7.606458 1
## 1487 #619CFF 3 0.24736295 0.34680918 0.34680918 12.368147 50 7.612329 1
## 1488 #619CFF 3 0.24728639 0.34670184 0.34670184 12.364320 50 7.618200 1
## 1489 #619CFF 3 0.24714276 0.34650047 0.34650047 12.357138 50 7.624070 1
## 1490 #619CFF 3 0.24693991 0.34621607 0.34621607 12.346995 50 7.629941 1
## 1491 #619CFF 3 0.24669492 0.34587259 0.34587259 12.334746 50 7.635812 1
## 1492 #619CFF 3 0.24635742 0.34539940 0.34539940 12.317871 50 7.641683 1
## 1493 #619CFF 3 0.24596133 0.34484408 0.34484408 12.298067 50 7.647554 1
## 1494 #619CFF 3 0.24551420 0.34421719 0.34421719 12.275710 50 7.653425 1
## 1495 #619CFF 3 0.24495204 0.34342902 0.34342902 12.247602 50 7.659295 1
## 1496 #619CFF 3 0.24433454 0.34256328 0.34256328 12.216727 50 7.665166 1
## 1497 #619CFF 3 0.24364620 0.34159821 0.34159821 12.182310 50 7.671037 1
## 1498 #619CFF 3 0.24284399 0.34047349 0.34047349 12.142200 50 7.676908 1
## 1499 #619CFF 3 0.24198200 0.33926495 0.33926495 12.099100 50 7.682779 1
## 1500 #619CFF 3 0.24102852 0.33792814 0.33792814 12.051426 50 7.688650 1
## 1501 #619CFF 3 0.23996774 0.33644091 0.33644091 11.998387 50 7.694521 1
## 1502 #619CFF 3 0.23884396 0.33486534 0.33486534 11.942198 50 7.700391 1
## 1503 #619CFF 3 0.23760858 0.33313331 0.33313331 11.880429 50 7.706262 1
## 1504 #619CFF 3 0.23627699 0.33126638 0.33126638 11.813849 50 7.712133 1
## 1505 #619CFF 3 0.23488049 0.32930846 0.32930846 11.744025 50 7.718004 1
## 1506 #619CFF 3 0.23335420 0.32716856 0.32716856 11.667710 50 7.723875 1
## 1507 #619CFF 3 0.23174615 0.32491403 0.32491403 11.587308 50 7.729746 1
## 1508 #619CFF 3 0.23007267 0.32256776 0.32256776 11.503633 50 7.735616 1
## 1509 #619CFF 3 0.22825441 0.32001852 0.32001852 11.412720 50 7.741487 1
## 1510 #619CFF 3 0.22637097 0.31737789 0.31737789 11.318548 50 7.747358 1
## 1511 #619CFF 3 0.22440881 0.31462689 0.31462689 11.220440 50 7.753229 1
## 1512 #619CFF 3 0.22231952 0.31169765 0.31169765 11.115976 50 7.759100 1
## 1513 #619CFF 3 0.22016832 0.30868162 0.30868162 11.008416 50 7.764971 1
## 1514 #619CFF 3 0.21792727 0.30553961 0.30553961 10.896364 50 7.770841 1
## 1515 #619CFF 3 0.21558042 0.30224927 0.30224927 10.779021 50 7.776712 1
## 1516 #619CFF 3 0.21317530 0.29887722 0.29887722 10.658765 50 7.782583 1
## 1517 #619CFF 3 0.21067317 0.29536917 0.29536917 10.533658 50 7.788454 1
## 1518 #619CFF 3 0.20808714 0.29174349 0.29174349 10.404357 50 7.794325 1
## 1519 #619CFF 3 0.20544755 0.28804272 0.28804272 10.272377 50 7.800196 1
## 1520 #619CFF 3 0.20270853 0.28420254 0.28420254 10.135426 50 7.806067 1
## 1521 #619CFF 3 0.19990675 0.28027437 0.28027437 9.995337 50 7.811937 1
## 1522 #619CFF 3 0.19705708 0.27627906 0.27627906 9.852854 50 7.817808 1
## 1523 #619CFF 3 0.19411073 0.27214821 0.27214821 9.705536 50 7.823679 1
## 1524 #619CFF 3 0.19112086 0.26795634 0.26795634 9.556043 50 7.829550 1
## 1525 #619CFF 3 0.18808332 0.26369762 0.26369762 9.404166 50 7.835421 1
## 1526 #619CFF 3 0.18496969 0.25933223 0.25933223 9.248484 50 7.841292 1
## 1527 #619CFF 3 0.18182268 0.25492005 0.25492005 9.091134 50 7.847162 1
## 1528 #619CFF 3 0.17862974 0.25044347 0.25044347 8.931487 50 7.853033 1
## 1529 #619CFF 3 0.17538474 0.24589389 0.24589389 8.769237 50 7.858904 1
## 1530 #619CFF 3 0.17211387 0.24130804 0.24130804 8.605693 50 7.864775 1
## 1531 #619CFF 3 0.16880339 0.23666668 0.23666668 8.440170 50 7.870646 1
## 1532 #619CFF 3 0.16546144 0.23198117 0.23198117 8.273072 50 7.876517 1
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## 1536 #619CFF 3 0.15189397 0.21295923 0.21295923 7.594699 50 7.900000 1
## group violinwidth width xmin xmax ymax weight colour size alpha
## 1 1 0.19051206 0.9 0.55 1.45 4.300000 1 grey20 0.5 0.3
## 2 1 0.19391124 0.9 0.55 1.45 4.302935 1 grey20 0.5 0.3
## 3 1 0.19730335 0.9 0.55 1.45 4.305871 1 grey20 0.5 0.3
## 4 1 0.20069036 0.9 0.55 1.45 4.308806 1 grey20 0.5 0.3
## 5 1 0.20406186 0.9 0.55 1.45 4.311742 1 grey20 0.5 0.3
## 6 1 0.20742640 0.9 0.55 1.45 4.314677 1 grey20 0.5 0.3
## 7 1 0.21077545 0.9 0.55 1.45 4.317613 1 grey20 0.5 0.3
## 8 1 0.21410891 0.9 0.55 1.45 4.320548 1 grey20 0.5 0.3
## 9 1 0.21743257 0.9 0.55 1.45 4.323483 1 grey20 0.5 0.3
## 10 1 0.22072808 0.9 0.55 1.45 4.326419 1 grey20 0.5 0.3
## 11 1 0.22401233 0.9 0.55 1.45 4.329354 1 grey20 0.5 0.3
## 12 1 0.22727356 0.9 0.55 1.45 4.332290 1 grey20 0.5 0.3
## 13 1 0.23051114 0.9 0.55 1.45 4.335225 1 grey20 0.5 0.3
## 14 1 0.23373588 0.9 0.55 1.45 4.338160 1 grey20 0.5 0.3
## 15 1 0.23692131 0.9 0.55 1.45 4.341096 1 grey20 0.5 0.3
## 16 1 0.24009215 0.9 0.55 1.45 4.344031 1 grey20 0.5 0.3
## 17 1 0.24323493 0.9 0.55 1.45 4.346967 1 grey20 0.5 0.3
## 18 1 0.24634713 0.9 0.55 1.45 4.349902 1 grey20 0.5 0.3
## 19 1 0.24944383 0.9 0.55 1.45 4.352838 1 grey20 0.5 0.3
## 20 1 0.25249495 0.9 0.55 1.45 4.355773 1 grey20 0.5 0.3
## 21 1 0.25552798 0.9 0.55 1.45 4.358708 1 grey20 0.5 0.3
## 22 1 0.25853052 0.9 0.55 1.45 4.361644 1 grey20 0.5 0.3
## 23 1 0.26149727 0.9 0.55 1.45 4.364579 1 grey20 0.5 0.3
## 24 1 0.26444677 0.9 0.55 1.45 4.367515 1 grey20 0.5 0.3
## 25 1 0.26734854 0.9 0.55 1.45 4.370450 1 grey20 0.5 0.3
## 26 1 0.27022942 0.9 0.55 1.45 4.373386 1 grey20 0.5 0.3
## 27 1 0.27307976 0.9 0.55 1.45 4.376321 1 grey20 0.5 0.3
## 28 1 0.27589133 0.9 0.55 1.45 4.379256 1 grey20 0.5 0.3
## 29 1 0.27868512 0.9 0.55 1.45 4.382192 1 grey20 0.5 0.3
## 30 1 0.28143239 0.9 0.55 1.45 4.385127 1 grey20 0.5 0.3
## 31 1 0.28415721 0.9 0.55 1.45 4.388063 1 grey20 0.5 0.3
## 32 1 0.28685355 0.9 0.55 1.45 4.390998 1 grey20 0.5 0.3
## 33 1 0.28951056 0.9 0.55 1.45 4.393933 1 grey20 0.5 0.3
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## 36 1 0.29732279 0.9 0.55 1.45 4.402740 1 grey20 0.5 0.3
## 37 1 0.29987282 0.9 0.55 1.45 4.405675 1 grey20 0.5 0.3
## 38 1 0.30238548 0.9 0.55 1.45 4.408611 1 grey20 0.5 0.3
## 39 1 0.30488276 0.9 0.55 1.45 4.411546 1 grey20 0.5 0.3
## 40 1 0.30734365 0.9 0.55 1.45 4.414481 1 grey20 0.5 0.3
## 41 1 0.30978352 0.9 0.55 1.45 4.417417 1 grey20 0.5 0.3
## 42 1 0.31220312 0.9 0.55 1.45 4.420352 1 grey20 0.5 0.3
## 43 1 0.31458957 0.9 0.55 1.45 4.423288 1 grey20 0.5 0.3
## 44 1 0.31696317 0.9 0.55 1.45 4.426223 1 grey20 0.5 0.3
## 45 1 0.31930778 0.9 0.55 1.45 4.429159 1 grey20 0.5 0.3
## 46 1 0.32163424 0.9 0.55 1.45 4.432094 1 grey20 0.5 0.3
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## 48 1 0.32622958 0.9 0.55 1.45 4.437965 1 grey20 0.5 0.3
## 49 1 0.32850387 0.9 0.55 1.45 4.440900 1 grey20 0.5 0.3
## 50 1 0.33075706 0.9 0.55 1.45 4.443836 1 grey20 0.5 0.3
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## 53 1 0.33743358 0.9 0.55 1.45 4.452642 1 grey20 0.5 0.3
## 54 1 0.33963583 0.9 0.55 1.45 4.455577 1 grey20 0.5 0.3
## 55 1 0.34182455 0.9 0.55 1.45 4.458513 1 grey20 0.5 0.3
## 56 1 0.34400379 0.9 0.55 1.45 4.461448 1 grey20 0.5 0.3
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## 59 1 0.35049597 0.9 0.55 1.45 4.470254 1 grey20 0.5 0.3
## 60 1 0.35264691 0.9 0.55 1.45 4.473190 1 grey20 0.5 0.3
## 61 1 0.35479330 0.9 0.55 1.45 4.476125 1 grey20 0.5 0.3
## 62 1 0.35693721 0.9 0.55 1.45 4.479061 1 grey20 0.5 0.3
## 63 1 0.35907672 0.9 0.55 1.45 4.481996 1 grey20 0.5 0.3
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## 77 1 0.38919894 0.9 0.55 1.45 4.523092 1 grey20 0.5 0.3
## 78 1 0.39138884 0.9 0.55 1.45 4.526027 1 grey20 0.5 0.3
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## 80 1 0.39578683 0.9 0.55 1.45 4.531898 1 grey20 0.5 0.3
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## 82 1 0.40021781 0.9 0.55 1.45 4.537769 1 grey20 0.5 0.3
## 83 1 0.40244812 0.9 0.55 1.45 4.540705 1 grey20 0.5 0.3
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## 87 1 0.41145206 0.9 0.55 1.45 4.552446 1 grey20 0.5 0.3
## 88 1 0.41372891 0.9 0.55 1.45 4.555382 1 grey20 0.5 0.3
## 89 1 0.41601418 0.9 0.55 1.45 4.558317 1 grey20 0.5 0.3
## 90 1 0.41830763 0.9 0.55 1.45 4.561252 1 grey20 0.5 0.3
## 91 1 0.42061564 0.9 0.55 1.45 4.564188 1 grey20 0.5 0.3
## 92 1 0.42292965 0.9 0.55 1.45 4.567123 1 grey20 0.5 0.3
## 93 1 0.42525779 0.9 0.55 1.45 4.570059 1 grey20 0.5 0.3
## 94 1 0.42759575 0.9 0.55 1.45 4.572994 1 grey20 0.5 0.3
## 95 1 0.42994204 0.9 0.55 1.45 4.575930 1 grey20 0.5 0.3
## 96 1 0.43230490 0.9 0.55 1.45 4.578865 1 grey20 0.5 0.3
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## 98 1 0.43705872 0.9 0.55 1.45 4.584736 1 grey20 0.5 0.3
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## 1480 3 0.19903757 0.9 2.55 3.45 7.571233 1 grey20 0.5 0.3
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## 1508 3 0.18555043 0.9 2.55 3.45 7.735616 1 grey20 0.5 0.3
## 1509 3 0.18408403 0.9 2.55 3.45 7.741487 1 grey20 0.5 0.3
## 1510 3 0.18256506 0.9 2.55 3.45 7.747358 1 grey20 0.5 0.3
## 1511 3 0.18098260 0.9 2.55 3.45 7.753229 1 grey20 0.5 0.3
## 1512 3 0.17929762 0.9 2.55 3.45 7.759100 1 grey20 0.5 0.3
## 1513 3 0.17756271 0.9 2.55 3.45 7.764971 1 grey20 0.5 0.3
## 1514 3 0.17575534 0.9 2.55 3.45 7.770841 1 grey20 0.5 0.3
## 1515 3 0.17386263 0.9 2.55 3.45 7.776712 1 grey20 0.5 0.3
## 1516 3 0.17192293 0.9 2.55 3.45 7.782583 1 grey20 0.5 0.3
## 1517 3 0.16990500 0.9 2.55 3.45 7.788454 1 grey20 0.5 0.3
## 1518 3 0.16781940 0.9 2.55 3.45 7.794325 1 grey20 0.5 0.3
## 1519 3 0.16569061 0.9 2.55 3.45 7.800196 1 grey20 0.5 0.3
## 1520 3 0.16348163 0.9 2.55 3.45 7.806067 1 grey20 0.5 0.3
## 1521 3 0.16122203 0.9 2.55 3.45 7.811937 1 grey20 0.5 0.3
## 1522 3 0.15892381 0.9 2.55 3.45 7.817808 1 grey20 0.5 0.3
## 1523 3 0.15654762 0.9 2.55 3.45 7.823679 1 grey20 0.5 0.3
## 1524 3 0.15413634 0.9 2.55 3.45 7.829550 1 grey20 0.5 0.3
## 1525 3 0.15168660 0.9 2.55 3.45 7.835421 1 grey20 0.5 0.3
## 1526 3 0.14917550 0.9 2.55 3.45 7.841292 1 grey20 0.5 0.3
## 1527 3 0.14663748 0.9 2.55 3.45 7.847162 1 grey20 0.5 0.3
## 1528 3 0.14406242 0.9 2.55 3.45 7.853033 1 grey20 0.5 0.3
## 1529 3 0.14144537 0.9 2.55 3.45 7.858904 1 grey20 0.5 0.3
## 1530 3 0.13880746 0.9 2.55 3.45 7.864775 1 grey20 0.5 0.3
## 1531 3 0.13613761 0.9 2.55 3.45 7.870646 1 grey20 0.5 0.3
## 1532 3 0.13344237 0.9 2.55 3.45 7.876517 1 grey20 0.5 0.3
## 1533 3 0.13073247 0.9 2.55 3.45 7.882387 1 grey20 0.5 0.3
## 1534 3 0.12799929 0.9 2.55 3.45 7.888258 1 grey20 0.5 0.3
## 1535 3 0.12525406 0.9 2.55 3.45 7.894129 1 grey20 0.5 0.3
## 1536 3 0.12250039 0.9 2.55 3.45 7.900000 1 grey20 0.5 0.3
## linetype
## 1 solid
## 2 solid
## 3 solid
## 4 solid
## 5 solid
## 6 solid
## 7 solid
## 8 solid
## 9 solid
## 10 solid
## 11 solid
## 12 solid
## 13 solid
## 14 solid
## 15 solid
## 16 solid
## 17 solid
## 18 solid
## 19 solid
## 20 solid
## 21 solid
## 22 solid
## 23 solid
## 24 solid
## 25 solid
## 26 solid
## 27 solid
## 28 solid
## 29 solid
## 30 solid
## 31 solid
## 32 solid
## 33 solid
## 34 solid
## 35 solid
## 36 solid
## 37 solid
## 38 solid
## 39 solid
## 40 solid
## 41 solid
## 42 solid
## 43 solid
## 44 solid
## 45 solid
## 46 solid
## 47 solid
## 48 solid
## 49 solid
## 50 solid
## 51 solid
## 52 solid
## 53 solid
## 54 solid
## 55 solid
## 56 solid
## 57 solid
## 58 solid
## 59 solid
## 60 solid
## 61 solid
## 62 solid
## 63 solid
## 64 solid
## 65 solid
## 66 solid
## 67 solid
## 68 solid
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## 1531 solid
## 1532 solid
## 1533 solid
## 1534 solid
## 1535 solid
## 1536 solid
##
## $data[[2]]
## colour x y PANEL group shape size fill alpha stroke
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##
##
## $layout
## <ggproto object: Class Layout, gg>
## coord: <ggproto object: Class CoordCartesian, Coord, gg>
## aspect: function
## backtransform_range: function
## clip: on
## default: TRUE
## distance: function
## expand: TRUE
## is_free: function
## is_linear: function
## labels: function
## limits: list
## modify_scales: function
## range: function
## render_axis_h: function
## render_axis_v: function
## render_bg: function
## render_fg: function
## setup_data: function
## setup_layout: function
## setup_panel_params: function
## setup_params: function
## transform: function
## super: <ggproto object: Class CoordCartesian, Coord, gg>
## coord_params: list
## facet: <ggproto object: Class FacetNull, Facet, gg>
## compute_layout: function
## draw_back: function
## draw_front: function
## draw_labels: function
## draw_panels: function
## finish_data: function
## init_scales: function
## map_data: function
## params: list
## setup_data: function
## setup_params: function
## shrink: TRUE
## train_scales: function
## vars: function
## super: <ggproto object: Class FacetNull, Facet, gg>
## facet_params: list
## finish_data: function
## get_scales: function
## layout: data.frame
## map_position: function
## panel_params: list
## panel_scales_x: list
## panel_scales_y: list
## render: function
## render_labels: function
## reset_scales: function
## setup: function
## setup_panel_params: function
## train_position: function
## xlabel: function
## ylabel: function
## super: <ggproto object: Class Layout, gg>
##
## $plot
##
## attr(,"class")
## [1] "ggplot_built"Finally, when looking around the internet for help, make sure that the version number is kind of close to the one you’re working with. The tidyverse has been around for some time now, and ggplot2 has gone through many iterations over the years.
The End!
You made it to the end! To celebrate, check out the new pupR package that allows you to look at adorable pictures of dogs in RStudio! Here’s a demo of how it works.
Also, for those of you who get bored and want to not work, here’s a fun xkcd plot we made! Just as a warning - the font import takes some time to complete.
#to be honest, setting up the xkcd package was more annoying than I
# thought it would be, but after messing with it for ~20mins it worked!
#install.packages("xkcd")
#install.packages("extrafont")
library(xkcd)## Loading required package: extrafont## Registering fonts with Rlibrary(extrafont)
download.file("http://simonsoftware.se/other/xkcd.ttf", dest="xkcd.ttf", mode="wb")
font_import()## Importing fonts may take a few minutes, depending on the number of fonts and the speed of the system.
## Continue? [y/n]## Exiting.loadfonts()## .Keyboard already registered with pdfFonts().## More than one version of regular/bold/italic found for System Font. Skipping setup for this font.## Andale Mono already registered with pdfFonts().## More than one version of regular/bold/italic found for Apple Braille. Skipping setup for this font.## AppleMyungjo already registered with pdfFonts().## Arial Black already registered with pdfFonts().## More than one version of regular/bold/italic found for Arial. Skipping setup for this font.## Arial Narrow already registered with pdfFonts().## Arial Rounded MT Bold already registered with pdfFonts().## Arial Unicode MS already registered with pdfFonts().## Batang already registered with pdfFonts().## Bodoni Ornaments already registered with pdfFonts().## Bodoni 72 Smallcaps already registered with pdfFonts().## Bookshelf Symbol 7 already registered with pdfFonts().## No regular (non-bold, non-italic) version of . Skipping setup for this font.## More than one version of regular/bold/italic found for Brush Script MT. Skipping setup for this font.## Calibri already registered with pdfFonts().## Cambria already registered with pdfFonts().## Cambria Math already registered with pdfFonts().## Candara already registered with pdfFonts().## Comic Sans MS already registered with pdfFonts().## Consolas already registered with pdfFonts().## Constantia already registered with pdfFonts().## Corbel already registered with pdfFonts().## Courier New already registered with pdfFonts().## No regular (non-bold, non-italic) version of DIN Alternate. Skipping setup for this font.## No regular (non-bold, non-italic) version of DIN Condensed. Skipping setup for this font.## Franklin Gothic Book already registered with pdfFonts().## Franklin Gothic Medium already registered with pdfFonts().## Gabriola already registered with pdfFonts().## Georgia already registered with pdfFonts().## Gill Sans MT already registered with pdfFonts().## Gulim already registered with pdfFonts().## Impact already registered with pdfFonts().## Khmer Sangam MN already registered with pdfFonts().## Lao Sangam MN already registered with pdfFonts().## Lucida Console already registered with pdfFonts().## Lucida Sans Unicode already registered with pdfFonts().## Luminari already registered with pdfFonts().## Marlett already registered with pdfFonts().## Meiryo already registered with pdfFonts().## Microsoft Yi Baiti already registered with pdfFonts().## Microsoft Himalaya already registered with pdfFonts().## Microsoft Sans Serif already registered with pdfFonts().## Microsoft Tai Le already registered with pdfFonts().## MingLiU_HKSCS-ExtB already registered with pdfFonts().## MingLiU_HKSCS already registered with pdfFonts().## MingLiU already registered with pdfFonts().## MingLiU-ExtB already registered with pdfFonts().## Mongolian Baiti already registered with pdfFonts().## MS Gothic already registered with pdfFonts().## MS Mincho already registered with pdfFonts().## MS PGothic already registered with pdfFonts().## MS PMincho already registered with pdfFonts().## MS Reference Sans Serif already registered with pdfFonts().## MS Reference Specialty already registered with pdfFonts().## Palatino Linotype already registered with pdfFonts().## Perpetua already registered with pdfFonts().## PMingLiU already registered with pdfFonts().## PMingLiU-ExtB already registered with pdfFonts().## SimHei already registered with pdfFonts().## SimSun already registered with pdfFonts().## SimSun-ExtB already registered with pdfFonts().## Tahoma already registered with pdfFonts().## More than one version of regular/bold/italic found for Times New Roman. Skipping setup for this font.## Trattatello already registered with pdfFonts().## Trebuchet MS already registered with pdfFonts().## Tw Cen MT already registered with pdfFonts().## More than one version of regular/bold/italic found for Verdana. Skipping setup for this font.## Webdings already registered with pdfFonts().## More than one version of regular/bold/italic found for Wingdings. Skipping setup for this font.## More than one version of regular/bold/italic found for Wingdings 2. Skipping setup for this font.## More than one version of regular/bold/italic found for Wingdings 3. Skipping setup for this font.set.seed(1234) #this makes sure the random numbers generated will be the same
df <- data.frame(vacc = rnorm(50, sd = .75), #make fake data
autism = rnorm(50, sd = .55))
xrange <- c(-2,2) # specific for xkcd
yrange <- c(-2,2) # specific for xkcd
ratioxy <- diff(xrange) / diff(yrange) # specific for xkcd
mapping <- aes(x, y, # specific for xkcd
scale,
ratioxy,
angleofspine ,
anglerighthumerus,
anglelefthumerus,
anglerightradius,
angleleftradius,
anglerightleg,
angleleftleg,
angleofneck)## Warning: Duplicated aesthetics after name standardisation:dataman <- data.frame(x= 1.75, y=-.4, # specific for xkcd
scale = .5,
ratioxy = 1,
angleofspine = -pi/2 ,
anglerighthumerus = c((7*pi)/4),
anglelefthumerus = c((5*pi)/4), # use the charts!!
anglerightradius = c(pi/6),
angleleftradius = c((5*pi)/6),
angleleftleg = 3*pi/2 + pi / 12 ,
anglerightleg = 3*pi/2 - pi / 12,
angleofneck = runif(1, 3*pi/2-pi/10, 3*pi/2+pi/10))
# finally, something you know -- ggplot!
ggplot(df, aes(x = vacc, y = autism, group = 1)) +
geom_point() +
geom_smooth(color = "red", method = "lm", se = FALSE) +
xkcdaxis(xrange, yrange) +
xkcdman(mapping, dataman) +
annotate("text", x=0, y = -1.75,
label = "The relationship between vaccines
and autism is as flat as the earth",
family="Arial", size = 3) +
labs(y = "Autism",
x = "Vaccines",
title = "Which conspiracy theory\nshould you believe??") +
theme_xkcd() +
theme(text = element_text(family = "Arial", size = 11))## Warning in theme_xkcd(): Not xkcd fonts installed! See vignette("xkcd-intro")## Warning in theme_xkcd(): Not xkcd fonts installed! See vignette("xkcd-intro")